Photoelectric Detection on Derived Attributes of Targets 9789819941568, 9789819941575

Citation preview

Xing Yang Yihua Hu

Photoelectric Detection on Derived Attributes of Targets

Photoelectric Detection on Derived Attributes of Targets

Xing Yang · Yihua Hu

Photoelectric Detection on Derived Attributes of Targets

Xing Yang National University of Defense Technology Hefei, China

Yihua Hu National University of Defense Technology Hefei, China

ISBN 978-981-99-4156-8 ISBN 978-981-99-4157-5 (eBook) https://doi.org/10.1007/978-981-99-4157-5 Jointly published with National Defense Industry Press The print edition is not for sale in China (Mainland). Customers from China (Mainland) please order the print book from: National Defense Industry Press. © National Defense Industry Press 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publishers, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publishers nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publishers remain neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Target detection refers to measures taken to obtain target features, particularly information (personnel, equipment, flora and fauna, topographic strata, and petroleum) of typical targets in military, geological, marine, and other fields. It is an important guarantee for the identification and accurate location of targets. Target detection has fully played its role in supporting information acquisition from the earliest detection means relying on human to those represented by simple devices such as telescopes used several centuries ago and to informationalized detection equipment based on electromagnetic waves (EMWs) widely used in modern technologies. During the Second World War, the British army built the early radar defense networks on the coastline, which allowed the British to successfully beat back devastating air assaults and undersea attacks of the German army. Since then, the radar detection and alarm technology have been rapidly concerned by various countries, which ushered in a period of rapid development of the technology. So far, detection radar systems in different institutions have been listed as the main detection equipment of various army services. Since 1960s, the modes and means of wars have changed profoundly. Modern combat platforms, such as satellites, airplanes, warships, and panzers, have been generally equipped with photoelectric devices such as the visible-light photographic system, forward-looking infrared system, thermal infrared imager, and laser rangefinder. These photoelectric detection devices lay the foundation of the battlefield information surveillance system and constitute the support system for modern warfare. At present, photoelectric detection technologies have been widely applied to modern battlefields. Especially, the increasingly harsh electromagnetic environment and the rapidly developing electronic countermeasures seriously limit the performance of radar detection systems with the working frequency lower than 300 GHz. In such context, all kinds of photoelectric detection systems have been intensively applied to battlefields as complementary and alternative means. Photoelectric detection has advantages including high imaging resolution, clear and intuitive target images, good concealment, wide sphere of action, strong anti-electromagnetic interference performance, complete working frequency range, and flexible application modes. Therefore, photoelectric detection has stood out in the high-tech field of target detection. v

vi

Preface

As the advantages of photoelectric detection in information acquisition become increasingly prominent, all kinds of photoelectric anti-detection measures and devices have developed apace. For example, anti-detection technologies including stealth, deception, and camouflage have been applied to the land, marine, space, and air combat platforms, so the development of photoelectric detection technologies has been faced with unprecedented challenges. At present, most conventional photoelectric detection methods can only detect optical signals reflected or radiated by targets themselves to measure the structural forms and motion features of targets, so as to determine target attributes. The detection effect is obviously affected by stealth, deception, camouflage, and locomotion of targets. In fact, many derived attributes other than targets themselves are directly correlated with target attributes. Derived attributes of targets are defined as accessory constituents of targets, incidental attributes of targets, or integrated features of targets that all can basically reflect essential features of targets, other than the structural forms, motion attributes, and overall electromagnetic characteristics of targets. These are exemplified by atmospheric wind-field disturbances and trailing vortexes induced by aerial moving targets, disturbances of the atmospheric compositional field induced by aerial moving targets, retroreflectors or marks on target platforms, airborne/spaceborne integrated hyperspectral synthetic attributes, and smells or wake bubbles of warships. Studying photoelectric detection technologies of derived attributes of targets is of important significance for exploring new detection modes and improving comprehensive performance including anti-stealth, anti-deception, anti-camouflage, and antijamming performance of detection devices. At present, research on the detection of derived attributes of targets in China and abroad is still in its infancy. Especially, the detection methods and devices warrant comprehensive and in-depth research. The authors and their research team have been engaged in photoelectric detection of derived attributes of targets since the early 2000s, presided over a number of national defense research projects, and organized or participated in the development of a variety of detection systems for derived attributes of targets. On this basis, ample firsthand data have been accumulated and a series of research papers have been published. The monograph was published after the authors summarized years of research findings, expecting to introduce the concepts and connotations of derived attributes of targets and discuss the principles and realization paths of several typical photoelectric detection methods for derived attributes of targets. The authors attempt to provide an effective basis for the application and research into photoelectric detection technologies of derived attributes of targets and provide reference for researchers dedicated to photoelectric detection and graduate students aspiring to this field. Starting from the concepts and connotations of detection technologies of derived attributes of targets, the monograph focuses on introduction of the basic principles, technological realization, and confirmatory experiment of photoelectric detection methods for several typical derived attributes of targets. The contents can be divided into four parts, including six chapters. Chapter 1 introduces the proposing background, concepts and connotations, generation and classification, and typical detection methods of derived attributes of targets. Chapters 2 and 3 highlight laser detection of derived attributes of aerial moving targets, which involves three derived

Preface

vii

attributes, namely atmospheric wind-field disturbances, trailing vortexes, and disturbances of the atmospheric compositional field. Chapters 4 and 5 systematically expound visible-light imaging detection of two derived attributes of targets, namely, retroreflectors and marks. Aiming at the key to the detection of synthetic attributes in images, Chapter 6 focuses on the detection and processing of synthetic attributes in airborne/spaceborne integrated images of typical ground and sea-surface targets, which are taken as a typical example. Since the beginning of the twenty-first century, academicians Ling Yongshun and Lv Yueguang have always provided careful guidance and strong support for the research work in the monograph; academicians Guo Guangcan and Liu Wenqing put forward very valuable suggestions for the research work and manuscript and also provided encouragement and support when the authors drafted the manuscript. Some research work in the monograph was completed in College of Electronic Countermeasures, National University of Defense Technology (the former Electronic Engineering Institute), and the work was concerned and assisted by college leaders as well as experts including Lei Wuhu, Hao Shiqi, Zhao Nanxiang, Fang Shengliang, Yang Hua, and Lu Yuan. Members of the authors’ research team and other colleagues as well as graduate studies also participated in some research and proposed useful suggestions for the completion of the monograph. In the writing process of the monograph, Shi Liang, Xu Shilong, Zheng Chao, Tao Huifeng, Dong Xiao, Li Le, Guo Liren, Gu Yu, Zhao Xinying, Liu Ying, Yu Lei, Xu Haoli, and Zhu Dongtao took part in data collection and typesetting. The authors express heartfelt thanks for their support, concern, and assistance. Much valuable Chinese and foreign literature has been referred to in the writing process. The authors hereby express sincere gratitude to authors of the literature. Because contents in the monograph are relatively new, some issues remain to be further discussed deeply and there might be some mistakes and flaws in this monograph due to limited knowledge of the authors. Comments from experts and readers are welcomed. Hefei, China December 2022

Xing Yang Yihua Hu

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Overview of Target Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 Target Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.2 Influences of Detection Technologies on Combat and Its Development Trend . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.3 Electronic and Acoustic Detection . . . . . . . . . . . . . . . . . . . . . . 1.1.4 Photoelectric Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Detection on Derived Attributes of Targets . . . . . . . . . . . . . . . . . . . . . 1.2.1 Concepts of Derived Attributes of Targets . . . . . . . . . . . . . . . 1.2.2 Connotations of Derived Attributes of Targets . . . . . . . . . . . . 1.2.3 Typical Photoelectric Detection Methods for Derived Attributes of Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Characteristics of Atmospheric Wind Fields and Acquisition of Disturbance Information of Targets . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Characteristics of Atmospheric Wind Fields . . . . . . . . . . . . . . 2.1.2 Principle of the Information Acquisition Algorithm of Moving Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Inversion Algorithm for Distribution of the Atmospheric Wind Field . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.4 Information Acquisition Algorithm of Moving Targets . . . . 2.1.5 Experimental Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 The Detection System for Target-Induced Atmospheric Wind-Field Disturbances and Performance Analysis . . . . . . . . . . . . . 2.2.1 Structure of the Detection System . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Performance Analysis of the Detection System . . . . . . . . . . . 2.3 Generation Mechanisms and Characteristics of the Disturbance Field of Trailing Vortexes . . . . . . . . . . . . . . . . . . .

1 1 1 2 5 7 13 13 14 16 20 23 23 24 25 26 31 33 34 35 35 40

ix

x

Contents

2.3.1 Generation Mechanisms of the Disturbance Field of Trailing Vortexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Analytical Model of the Disturbance Fields of Trailing Vortexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Characteristic Parameters of the Disturbance Field of Trailing Vortexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.4 Simulation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Detection of Trailing Vortexes Based on the Coherent Doppler Lidar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Coherent Doppler Laser Detection Principle of Aircraft Trailing Vortexes . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Modeling and Analysis of Doppler Spectra for Echoes from Trailing Vortexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Identification Trailing Vortexes of Airplanes and Parameter Extraction Based on Laser Echoes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 Preprocessing of Echoes of Lidar Detection . . . . . . . . . . . . . . 2.5.2 Identification of Trailing Vortexes of Airplanes Based on Doppler Spectral Characteristics . . . . . . . . . . . . . . . . . . . . . 2.5.3 Parameter Extraction of Trailing Vortexes of Airplanes Based on Laser Echoes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Laser Detection on Atmospheric Components Disturbed by Aerial Moving Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Laser Detection on Atmospheric CO2 Disturbance . . . . . . . . . . . . . . 3.1.1 Characteristics of Atmospheric CO2 Disturbance by Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Laser Detection Principles of the Atmospheric CO2 Disturbances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.3 DIAL Detection System Schemes . . . . . . . . . . . . . . . . . . . . . . 3.2 Detection of the Water–Vapor Concentration . . . . . . . . . . . . . . . . . . . 3.2.1 Detection Principle of the Water–Vapor Concentration . . . . . 3.2.2 Design of the DIAL Detection System . . . . . . . . . . . . . . . . . . 3.2.3 Performance Analysis and Simulation of Detection of the Water–Vapor Concentration . . . . . . . . . . . . . . . . . . . . . . 3.3 Analysis of Detection Performance for Disturbances of Atmospheric Component . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Empirical Mode Decomposition and Data Preprocessing Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Inversion Precision of Gas Concentrations . . . . . . . . . . . . . . . 3.3.3 Influences of Atmospheric Attenuation and Turbulences on Detection Performance . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

41 42 46 48 52 52 54 63 63 66 73 83 85 85 86 96 103 116 116 118 121 127 127 132 140 145

Contents

xi

4 Active Imaging Detection on Target Retroreflection Features . . . . . . . 4.1 Detection Principle of Retroreflection Features of Targets . . . . . . . . 4.2 Quantification and Realization of Retroreflection Features . . . . . . . . 4.2.1 Retroreflection Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Quantitative Modeling of Retroreflection Features . . . . . . . . 4.2.3 Analysis of the Active Imaging System . . . . . . . . . . . . . . . . . . 4.3 Retroreflector Detection and Extraction . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Analysis of Feature Salience . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Modeling Based on Feature Salience . . . . . . . . . . . . . . . . . . . . 4.3.3 Target Detection Based on Feature Salience . . . . . . . . . . . . . . 4.4 Retroreflector Detection Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Retroreflector Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Analysis and Design of the Active Imaging System . . . . . . . 4.4.3 Target Detection Through Multifeature Fusion Based on Feature Salience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

147 147 148 148 152 155 155 155 157 163 170 170 171

5 Passive Imaging Detection on Identity Attributes of Targets . . . . . . . . 5.1 Grayscale Processing of Color-Discrete Characteristic . . . . . . . . . . . 5.1.1 Analysis of Grayscale Methods . . . . . . . . . . . . . . . . . . . . . . . . 5.1.2 Grayscale Processing Principle of Color-Discrete Characteristic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.3 Preprocessing of Grayscale . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Identification and Recognition Based on the Improved SIFT . . . . . . 5.2.1 Overview of the SIFT Operator . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Elliptical-Neighborhood SIFT Operator . . . . . . . . . . . . . . . . . 5.2.3 Identification Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.4 Experimental Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

185 186 186

6 Detection and Processing of Synthetic Attributes of Integrated Aerospace Images of Targets on the Ground and Sea Surface . . . . . . . 6.1 Modeling and Simulation of Detection Images Fusion Based on Integrated Aerospace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Spectral Dimensional Transformation Based on Integrated Aerospace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.2 Scale-Space Transformation Based on Integrated Aerospace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.3 Radiation Intensity Transformation Based on Integrated Aerospace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.4 Mixed Pixels Transformation Based on Integrated Aerospace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.5 Noise Transformation Based on Integrated Aerospace . . . . . 6.1.6 Simulation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Combined Ant Colony Optimization of Spatial-Spectral 2D Features in Detection Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

177 182

188 193 210 210 214 219 225 232 233 233 235 236 237 239 240 241 243

xii

Contents

6.2.1 Combined ACO of Spatial-Spectral 2D Features . . . . . . . . . . 6.2.2 Simulation Experiments and Analysis . . . . . . . . . . . . . . . . . . . 6.3 Classification of Detection Images Based on Artificial Immune Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Modeling and Kernel Space Mapping of AIN . . . . . . . . . . . . 6.3.2 AIN Training and Classification of Detection Images . . . . . . 6.3.3 Simulation Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Classification of Detection Images Based on k-Nearest Neighbor Simplex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 Proportions and Clustering Features of Samples . . . . . . . . . . 6.4.2 AC-std Metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.3 kNNS Based on AC-std . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.4 Simulation Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Synthetic Attribute Detection of Targets in Sea Background . . . . . . 6.5.1 Fusion Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.2 Algorithm Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.3 Simulation Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

244 251 260 260 264 268 275 275 277 281 286 293 293 294 296 301

Chapter 1

Introduction

1.1 Overview of Target Detection The rapid development of modern science and technology greatly improves the level and ability of detection technologies. Modern detection devices or systems mainly can be divided into electronic, photoelectric, and acoustic detection devices, which can be deployed on ground, on sea-surface, underwater, in air, and in space. By using high-performance detection systems, detection and observation in the whole time domain and large airspace of global coverage can be realized to rapidly, accurately, and comprehensively master needed information. All nations in the world have paid particular attention to the development of detection technologies and modern detection technologies have become an important field in military high technologies.

1.1.1 Target Detection 1.1.1.1

Basic Concept

Target detection technologies refer to technologies that are used to find, identify, observe, track, and locate targets. Modern detection systems are information assurance systems [1] that combine various high and new technologies and devices together according to the needs of application, so as to realize all kinds of detection objectives. Here, “finding” refers to target detection to determine whether a target appears or not. It extracts the target according to the discontinuity of the target and surrounding background and therefore determines that there is a target at a place. Theoretically, any physical object in the nature and its phenomena have some features that render the object different from the background. Any difference between a target and the background, including differences in appearance and shape, or differences in physical properties such as acoustic, optical, electrical, magnetic, thermal, and mechanical properties can be distinguished by sense organs of human © National Defense Industry Press 2023 X. Yang and Y. Hu, Photoelectric Detection on Derived Attributes of Targets, https://doi.org/10.1007/978-981-99-4157-5_1

1

2

1 Introduction

or using some technologies. This is the basis by which targets can be detected. Detection systems fulfill tasks according to features of targets, such as acoustic, optical, electrical, magnetic, thermal, and mechanical features. “Identification” means determining whether an object is the actual target or not and determining the types of targets. “Observation” refers to paying close attention to the movement of targets, particularly changes in the motion state. “Tracking” is mainly shown as continuous observation of targets. “Location” refers to detecting and determining locations of targets at a certain accuracy, namely the orientation, altitude and distance of targets. Therein, “finding” and “identification” are the basis of target detection and precondition for the smooth observation, tracking, and location. Generally, the five processes are all accompanied by analysis of movement states of targets, including the velocity, orientation, altitude, and distance. Results of these processes can be output individually or collectively to jointly serve as the basis for decision-making.

1.1.1.2

Types of Detection

Modern detection technologies can be classified using many methods, such as classification according to the motion airspace of carrying platforms, range of detection missions, means of detection activities, detection means, and technological principles for realizing detection and identification [2]. They can be divided into four types, namely ground, air, underwater, and space detection according to the motion airspace of carrying platforms. According to the means of detection activities, they are classified into three types, namely armed, intelligence, and technical detection. The technologies are divided into secret observation and eavesdropping, search and capture, firepower detection, photographic detection, radar detection, and radiosounding based on the detection means. Moreover, the classification according to technological principles for realizing detection and identification is the most common, by which the detection technologies can be classified into electronic detection, acoustic detection, and photoelectric detection. Therein, electronic detection includes radar detection and radiosounding; photoelectric detection involves ultraviolet (UV), visible-light, infrared (IR), laser, low-light, and multispectral detection.

1.1.2 Influences of Detection Technologies on Combat and Its Development Trend 1.1.2.1

Influences of Detection Technologies on Wars

The development and application of detection and surveillance technologies to battlefields have led to significant improvement of detection and surveillance means in battlefields. Detection means have been diversified and various means are used

1.1 Overview of Target Detection

3

together. They greatly improve the large-area surveillance capability, precision detection capability, around-the-clock detection capability at night or under complex conditions, real-time or near real-time detection capability, and the capability to recognize camouflage, so they exert profound influences on wars [3]. (1) Enlargement of battlespace Functioning as a clairvoyant eye and an extendable ear, modern detection technologies and devices can cover the whole battlefield and achieve worldwide, full-depth, large-area detection and surveillance. The detection depth of ground surveillance systems can reach more than 150 km; the flight of high-altitude detectors is as long as 4800 km, with the service time of 12 h and hourly surveillance range of 3.89 × 105 km2 . Satellite detection and surveillance can cover millions of square kilometers. The increasing combat distance provides conditions for outfighting, while also brings serious challenges to traditional close combats, so new combat methods have to be explored. (2) Improvement of information acquisition means The information acquisition equipment becomes more advanced and means are diversified. The development of detection technologies leads to revolution of the modern information acquisition means. Battlefield detection in the past mainly relies on artificial detection using visual observation equipment. However, information is acquired in modern warfare mainly using detection devices equipped with advanced optical, electrical, and magnetic sensors, so as to get battlefield information and provide a basis for setting various missions. The use of modern detection means can master the mission scenarios more comprehensively. (3) Improvement of management quality Information is more accurate and timeous. As the mission scenarios become more complicated and changeable, it is more important to timeously obtain high-quality information. After application of modern detection and surveillance technologies, particularly satellites and remote sensing technology to the military field, the scope of information acquisition is significantly enlarged, accompanied with the greatly improved speed and accuracy. In the Gulf War, to defend against Scud missiles of Iraq, early warning satellites of the US army can capture targets and judge the impact points within 90–120 s after firing of Scud missiles, and they are able to transmit the information to the air-defense missile troops at the gulf within 3 min. This provides prewarning time of 90–120 s and gains time for command and counterattack. The real-time, rapid, and accurate information transmission ability and means of high-tech detection devices substantially improve the timeliness of combat commands. Modern detection and surveillance systems not only can offer all-around colorful information in different distances that can be directly read, seen, and heard, but also provide assistance in calculation, analysis, and judgment by virtue of the logical function of computers. They can also formulate plans and schemes to carry out military counterwork simulations and compare feasibility of schemes, so as to choose the optimal scheme, which improves the quality of missions.

4

1 Introduction

(4) Promoting development of anti-detection technologies Some false battlefield targets can even be taken as real. The application of detection technologies to battlefields facilitates the development of anti-detection technologies. As a result, the battlefield becomes increasingly transparent and troops find them more difficult to perform covert operations, so new methods of camouflage and action have to be explored. For example, the commonly used camouflage methods are effective in visual detection and low-light detection, while they are ineffective after emerging of thermal imaging devices. The use of a large number of high-tech detection devices has posed a greater threat to the survival of battlefield targets. To improve the survivability of battlefield targets and achieve the suddenness of wars, anti-detection technologies have to be developed.

1.1.2.2

Development Trends of Detection Technologies

With the rapid technological development in the present world, modern detection and surveillance technologies have shown the following development trends: (1) Three-dimensional development in the space These technologies have developed to the whole airspace. Because of the abrupt increase in the firing range of modern weapons, the maneuverability of troops has been rapidly improved, so the modern battlefield has to be deep and threedimensional (3D). To cope with the characteristic, detection and surveillance systems need to be four-in-one systems comprising the space, air, ground (water-surface), and underwater systems. (2) Real-time transformation in terms of speed The modern detection and surveillance technologies should cover the whole time domain. Modern warfare is rapidly varying, which requires the time of detection and surveillance to be as short as possible. Therefore, the information processing and transmission speed becomes a keypoint. With the development of remote sensing technology and computer technology, automatic classification and recognition of remote sensing images centered on computers have to be used to improve the information processing speed. (3) Integration of detection means As the detection technologies are constantly improved, various anti-detection devices as well as camouflage and jamming techniques have developed. To identify camouflage and improve the detection effect, researchers need to speed up to develop new IR, laser, and microwave remote sensors and use a variety of remote sensors to observe a same area at the same time. This not only can obtain multiple types of information but also can improve the detection and surveillance effects.

1.1 Overview of Target Detection

5

(4) Integration of detection, surveillance, and attack systems Integration of detection, surveillance, and attack systems refers to combination of detection and surveillance systems with equipment to form a reasonable whole, in a bid to timeously find and attack targets. For example, some remotely operated aircraft carries detection, tracking, and aiming devices as well as projectiles, and they can rapidly destroy a target after discovering the target. (5) Improvement of survivability of detection and surveillance systems Emerging of various anti-detection devices, particularly precision guided weapons, poses a serious threat to detection and surveillance systems. The survivability of detection and surveillance systems has become an important factor of fulfilling missions. Therefore, improving the survivability of the whole detection and surveillance systems has become a new issue that needs to be addressed urgently.

1.1.3 Electronic and Acoustic Detection 1.1.3.1

Radio Signal Detection

Detection means that receive radio communication signals and radar signals are termed collectively as electronic detection. Therefore, electronic detection can be generally divided into radio signal detection (radiosounding) and radar detection. Radiosounding refers to intercepting and deciphering radio communication signals of the opposite side using radio receiving equipment, so as to figure out the configuration of radio communication devices of the opposite side. Radiosounding is characterized by the long detection range, fast speed, covert work, and less vulnerability to the environment, topography, and climate. It includes two aspects: one is interception, and the other is direction-finding (DF) and location. Radiosounding “interception” refers to detection relying on three laws, namely radio wave propagation, signals, and contact. The main interception equipment is radio receivers. According to the interception laws, the radiosounding interception can be divided into interception of signal and contact laws. Signals transmitted by radio waves are certainly in a certain form determined by the communication parties following a certain law. With regard to signal modulation, to make each radio signal represent certain information, the transmitted radio waves have to be modulated in a certain way. The modulation methods include amplitude, frequency, and phase modulations. Signals modulated using different methods have different spectral characteristics, so the corresponding different demodulation methods have to be used when receiving the signals. In terms of the combination of signals, although various radio signals can be combined in different forms, they are always represented by the presence (or absence), length, and magnitude of current pulses, which also change following certain laws. The same code combination may have different meanings in different languages. From the waveform, radio waves are also in different forms, such as continuous waves, interrupted waves, and rectangular waves. By analyzing different

6

1 Introduction

waveforms and ripples, technical parameters including the technical specifications, modulation modes, arrangement modes, and combination laws of communication signals of the enemy can be clarified. In radio communication, apart from radio waves carrying information, the communication elements and service terms such as the call sign, frequency, and contact time should also be specified. Interception of contact laws just uses these laws. Radio DF refers to determining the orientation of radio transmitting stations at work using directional radio receivers (also known as radio direction finders). DF can determine the orientation of communication stations and realize the detection objective. Radio DF can be classified into diverse types: it is divided into auditory DF and visual DF according to different display methods; into fixed DF, semi-fixed DF, and mobile DF according to different usage modes; into air DF, maritime DF, and ground DF according to usages of direction-finder sets; into long-wave DF, medium-wave DF, short-wave DF, and ultra-short-wave DF according to different applicable wavelengths.

1.1.3.2

Radar Detection

Radar detection is a detection method to find targets and determine target states (range, altitude, azimuth, and motion speed) according to reflection characteristics of radio waves by objects. It is currently the most developed and most widely used detection means. Radar detection is characterized by the long operating range, fast measurement, high precision, and around-the-clock use and therefore has been widely applied to battlefields and has become the core detection means in modern warfare. Radars have many types and diverse usages. According to different missions or usages, they can be divided into ➀ warning and director radars, including air-defense surveillance radar, sea defense radar, airborne early warning radar, over-the-horizon radar, and ballistic missile early warning radar; ➁ weapon control radars, including gun-pointing radar, missile-guidance radar, torpedo attack radar, airborne-intercept radar, airborne bombing radar, terminal guidance radar, and ballistic missile tracking radar; ➂ detection radars, including battlefield detection radar, emplacement and calibration radar, moving target detection and calibration radar, and detection and terrain display radar; ➃ navigation support radars, including navigational radar, marine navigation radar, terrain following/terrain avoidance radar, and landing (ship-based) radar.

1.1.3.3

Acoustic Detection

Acoustic waves are elastic waves that have different propagation speeds in different media. The target detection mode depending on the propagation characteristics of acoustic waves in different media is acoustic detection. Acoustic detection devices mainly include acoustic sensors and underwater detection equipment. Acoustic sensors are widely used and their sensors are a microphone, which is an acousticalelectrical transducer with the same working principle of microphones. The most

1.1 Overview of Target Detection

7

prominent advantage of acoustic sensors is the high resolution. The acoustic features of targets during motion can be reproduced after data processing. If the moving targets are human, it not only can hear sounds but also can figure out the nationality, identity, and conversation according to speech. If the moving targets are vehicles, the type of vehicles can be judged according to the sound. In addition, natural interference to acoustic sensors can also be eliminated. Acoustic sensors cover a large detection range, which is 40 m for normal human conversation and several hundreds of meters for moving vehicles. Sonars are devices to sense and detect underwater targets based on the underwater acoustic propagation characteristics. The propagation speed of acoustic waves in oceans is faster than 1450 m/s. The propagation speed of acoustic waves in water is affected by the temperature, salinity, and hydrostatic pressure (depth) of seawater. The higher the temperature is, the faster the acoustic speed; the increases in salinity and hydrostatic pressure may accelerate the acoustic speed. The propagation of acoustic waves in oceans mainly shows the following characteristics: transmission and diffraction, reflection and refraction, scattering and reverberation, attenuation, and sound tracks. Sonars can be used to search, measure, identify, and track dunkers and other underwater targets. Sonars can be divided into passive and active ones according to their working modes. Passive sonars are also known as noise-based sonars, which are mainly used to search acoustic waves from targets and have characteristics such as good concealment and security, strong target identification ability, and long detection range. However, they fail to detect quiescent targets and cannot provide distance from targets. Active sonars are also known as echo-ranging sonars, which are able to detect quiescent targets and can measure the orientation and distance. However, they are easily intercepted by the enemy and therefore exposed, and their detection range is also short. According to different users, sonars can be divided into surface ship sonar, submarine sonar, airborne sonar, and shore-based sonar.

1.1.4 Photoelectric Detection Since the 1960s, the modes and means of wars have undergone profound changes. Modern combat platforms such as satellites, airplanes, warships, and panzers have been generally equipped with photoelectric devices including the visible-light photographic system, forward-looking infrared system, thermal infrared imager, and laser rangefinder. These photoelectric devices lay the foundation of the battlefield information surveillance system and constitute the support system for modern warfare. At present, photoelectric detection technologies have been widely applied to modern battlefields. Especially, the increasingly harsh electromagnetic environment and the rapidly developing electronic countermeasures seriously limit the performance of radar detection systems with the working frequency lower than 300 GHz. In such context, various photoelectric detection systems have been intensively applied to battlefields as complementary and alternative means.

8

1.1.4.1

1 Introduction

Principles and Characteristics of Photoelectric Detection

Photoelectric detection is a military means to detect, identify, track, aim, and surveil targets based on the reflection of light sources on targets and background, or the difference in the light waves radiated by targets and background. It contains wide contents and all detection methods that apply signals in the optical frequency band produced by targets of the opposite side belongs to the domain. Photoelectric detection can be carried out on various platforms such as satellite-borne, airborne, water-surface, and land platforms, or operated by a single soldier. It is real-time or non-real-time and the detection objects can be any valuable target of the opposite side. The photoelectric detection means can be all kinds of devices that work in the optical frequency band. Photoelectric detection has the following advantages: it has high imaging resolution and provides intuitive and clear target images that are incomparable to other detection modes; it is dominated by passive detection and has good concealment that renders the devices difficult to be detected by the opposite side, and a relatively large sphere of action; it shows high anti-electromagnetic interference performance [4]. In environments of strong electromagnetic countermeasures where radars cannot work, photoelectric detection still can undertake the main detection tasks. Photoelectric detection performs in the complete working frequency range, which has covered IR, visible light, and UV frequencies. The flexible application mode allows wide application of photoelectric detection to platforms and occasions including satellites, unmanned detectors, fixed-wing aircraft, helicopters, motor vehicles on ground, fixed detection positions, and portable platforms for single soldiers. The technology can match with detection devices such as the radar, sonar, and electronic warfare system to form an integrated system. However, photoelectric detection also has three deficiencies: ➀ short wavelength of light waves. Light waves are easily influenced by adverse weather conditions such as rain, snow, fog, and sandstorm, and other factors, so that light energy is lost, and wave fronts and polarization states are distorted. ➁ Light propagates basically along straight lines and cannot diffract, so the light is likely to be sheltered and blocked by geographical conditions. ➂ Solid elements including photosensors on photovoltaic devices are generally vulnerable. The light intensity that can be received is limited. Once the light intensity exceeds the limit, photosensors may be saturated, overloaded, out of work, and even completely damaged. These problems will be gradually overcome with the advance of photoelectronic technologies. As a means that enables detection earlier than the enemy, photoelectronic detection overcomes the limitation of radars at the low altitude and can identify radar decoys [5].

1.1.4.2

Classification of Photoelectric Detection

According to the realization principles, photoelectric detection can be divided into visible-light detection, low-light detection, laser detection, UV detection, IR detection, and multispectral detection.

1.1 Overview of Target Detection

9

(1) Visible-light detection Electromagnetic waves (EMWs) can be classified into radio waves, microwaves, IR rays, visible light, UV light, X rays, and Y rays according to the different wavelengths or frequencies. The wavelength of visible light covers a range of 0.4–0.76 µm, and the different reflection characteristics of visible light by objects determine the colors of objects. The principle of visible-light detection is to directly observe or image targets by virtue of the reflection of visible light by targets, thus achieving the goals of detection, identification, tracking, and surveillance. Visible-light detection equipment is mainly used for detection in daytime, and it mainly includes optical observation instrument, optical camera, and daylight television, as well as the large visible-light camera equipped on sounding satellites. These devices have been mature in design and process and the principle cannot be further developed. Future development trend of these devices is improvement of the optical system and structure, so as to increase the operating range, and add image stabilizing systems and laser-proof coatings. In fact, some low-light and laser detection devices also use the visible-light band, while they are classified separately due to the special working principles. (2) Low-light detection Low-light detection is based on the principle of electron multiplication of the photoelectric effect. Low-light detection devices amplify low-light natural light including the night airglow, starlight, and airglow reflected by targets under conditions of a low illumination level by hundreds of thousands of times, so as to reach the goal of being applicable to observation or imaging at the low illumination level. Low-light detection devices work mainly using night airglow in the visible and near-IR band, which belongs to a passive mode of high concealment. At present, low-light detection devices in mainstream equipment belong to the second- and third-generation products. These devices are produced in the background that US researchers developed the microchannel plate image intensifier in the early 1970s and the third-generation lowlight-level image intensifier that uses the negative electron affinity gallium arsenide photocathode in the 1980s. They are represented by night vision goggles for pilots. At the beginning of the twenty-first century, low-light detection devices have been improved to the fourth-generation in the USA, and the luminance gain has reached tens of thousands to hundreds of thousands of times, as represented by AN/PVS night vision equipment of the land forces [6]. (3) Laser detection The emerging of the first laser in the 1960s brought the most profound and extensive shock to the military field. Since then, laser detection devices represented by laser rangefinders have developed apace. Laser rangefinders have developed to the third-generation, in which CO2 laser rangefinder, diode pumped solid-state laser rangefinder, new solid-state laser rangefinder, and Raman frequency-shift laser rangefinder are representative ones. The third-generation laser rangefinders working in the bands of 10.6 and 1.54 µm are safe for human eyes and have become an essential component of modern weapon systems [7]. Lidars, as active detection means,

10

1 Introduction

can fulfill missions that cannot be completed by ordinary radars apart from advantages including smaller antennas, higher angular, range, and speed resolutions, higher tracking and aiming precisions, and higher imaging resolution compared with ordinary radars. These mission include detection of stealth planes, dunkers, torpedoes, poison clouds, and biological warfare agents. Passive laser detection devices are mainly used for laser detection and alarm, and they have been generally installed on combat aircraft, warships, and ground key facilities to detect and warn targets by virtue of laser beams radiated or scattered by threatening laser sources. At present, laser detection has become an important detection means in battlefields and has been widely applied to ranging, velocity measurement, target imaging and recognition at night, precision target tracking and aiming, and precision weapon guidance [8]. Main laser detection devices include laser rangefinder, laser velocimeter, lidar, laser scanning camera, laser television, and laser target indicator. (4) UV detection and alarm UV light is EMWs with the wavelength in the range of 0.01–0.4 µm. UV detection and alarm is a detection technology to detect, locate, analyze, and judge incoming missile threats according to the UV radiation characteristics at the launching and entry of guided missiles of the enemy. UV detection and alarm devices can effectively eliminate all kinds of artificial and natural interference as well as non-approaching missiles in the battlefield environment and can detect incoming guided missiles with a low false-alarm rate. Under multithreat conditions, the priority of multiple threats can be rapidly established according to the degree of threat. General-type UV alarm is developed in the early period of UV detection and alarm. At present, UV imaging alarm has become a bright spot of rapid development in the photoelectric alarm field due to its significant combat effectiveness. Typical UV detection and alarm devices include the AN/AAR-54 system developed by the US and the MII DS-2 system developed jointly by Germany and France. IR detection refers to detecting targets based on the thermal radiation of objects in the IR band. Any object can release energy outwards as EMWs, that is, thermal radiation when its temperature is higher than the absolute zero. The thermal radiation energy of the same object is also distributed in different wavelengths at different temperatures. The higher the temperature is, the greater the total thermal radiation energy and the energy is mainly distributed in the side of a short wavelength. The higher the temperature is, the shorter the peak wavelength. Generally, the temperature of military targets is in the range of – 15–37 °C, and the radiation wavelength is in the range of 9–10 µm, in the IR band. The thermal radiation wavelength of most targets at the room temperature is in the IR band, so targets can be detected even at night by receiving IR radiation of objects. IR rays can be divided into the near-IR (0.76– 3 µm), mid-IR (3–6 µm), mid-far-IR (6–20 µm), and far-IR (20–1000 µm) ones, in which the near-IR, mid-IR, and mid-far-IR bands are used by various IR detection devices. IR detection devices are mainly divided into imaging and non-imaging ones. Imaging IR detectors mainly include IR cameras, IR night vision devices, and thermal imaging night vision apparatuses, while non-imaging IR detectors mainly include IR early warning detectors.

1.1 Overview of Target Detection

11

IR cameras share the same imaging method with ordinary visible-light cameras. The difference lies in that IR cameras need to use a special lens that only allows IR radiation to pass through and they adopt special IR photographic films sensitive to IR radiation. IR photographic films fall in two types: black-and-white IR films and color IR films. Camouflage can be identified and concealed targets can be found according to tonal variation of black-and-white IR photographs and color variation of color IR photographs. Compared with visible-light cameras, IR cameras have another advantage: they can take long-distance shots under adverse conditions at night or with thick fog. At present, IR photographic films sensitive to near-IR band can only be produced. Because objects have weak IR radiation at room temperature, intense light sources have to be used to irradiate targets during photography at night. Because IR photographic films sensitive to near-IR radiation can only be produced so far while military targets generally have strong mid-IR and far-IR radiation, which are difficult to be photographed and detected, thermography has emerged. By using the optical scanning technique and semiconductor materials that are sensitive to mid-IR and far-IR radiation, commonly used IR scanning devices convert IR energy radiated by ground objects into electrical signals, which are then processed, amplified, and transformed into visible-light images. Moreover, IR sensors are passive IR detectors. When a target passes by, the IR probe absorbs IR radiation of the target, by which targets within 20–50 m of a viewing angle sector can be found [9]. (5) Multispectral detection Multispectral detection is a detection means to detect, identify, track, and surveil targets based on differences in optical spectra and radiation energy of objects by photographing or scanning the same target simultaneously in different spectral bands and then processing and analyzing the obtained images. For example, flourishing living plants are red, cut plants for camouflage are blue, while metal objects painted green are black in multispectral images. Limited by spectral responses of photographic films, multispectral cameras generally work in the band of 0.35–0.9 µm, at least not beyond 1.35 µm. Multispectral imaging can fully reveal implicit spectral reflection characteristics of targets. Spectral analysis of multiple images can further reflect the real characteristics of targets and improve the detection and identification ability of photovoltaic devices for targets. The GOES photoelectric detection system produced by Lockheed Martin Corporation uses multispectral sensors and can provide high-resolution videos and IR images in seven bands. Generally obtained using apertures, prisms, or grating spectrometers, hyperspectral images can offer very narrow spectral characteristics for identifying special targets or enable adaptive measurement of a few or wide frequency bands. Multispectral sensors first emerged in the Balkan Conflict and its optimal usage is to find artificial targets against the natural background. At present, the fourth-generation AURORA hyperspectral sensor delivered by BAE Systems to the US army contains a hyperspectral sensor with the resolution of six million pixels, which is amounted on the improvised explosive device of RQ27 Shadow. The TRWIS23 hyperspectral imager produced by TRW Inc. in the USA works in the waveband of 014–215 µm and has 84 continuous spectral channels.

12

1 Introduction

The visible and near-IR bandwidth is only 5 nm, and the short-wave IR is also only 6125 nm, showing a high signal-to-noise ratio, which imparts extremely strong target discrimination capability to the imager. In addition, the high spectral resolution is important for identifying camouflage, friend or foe identification, target classification, and mine detection. The hyperspectral imager has been installed on Shadow 200, Hunter, Predator, or Global Hawk [10].

1.1.4.3

Challenges for Photoelectric Detection

As photoelectric detection shows increasingly significant advantages in information acquisition, various photoelectric anti-detection measures and devices have developed rapidly, which have played an important role in protecting important targets of one’s own side. The primary challenge for photoelectric detection is the photoelectric anti-detection measures and devices. Photoelectric anti-detection is to make facilities of one’s own side undetectable by photoelectric detection devices of the enemy by paying attention to weak links of photoelectric detection systems, so that the enemy comes up with nothing. Photoelectric anti-detection is divided into the active and passive ones, and mainly uses three measures, namely camouflage and stealth, shield, and deception. The three photoelectric anti-detection measures can complement for each other. Ideal camouflage and stealth should make targets of one’s own side undetectable by photoelectric detection systems and IR seekers; however, the ideal effect generally cannot be reached. Generally, targets need to reach a certain degree of stealth, and then deception is expected to play its role. IR smoke screens need to have strong absorption of IR radiation. However, smoke screens will also block the view of one’s own IR system. Photoelectric anti-detection technologies mainly include smoke screens, chaff, camouflage, stealth, decoys, and destruction and blinding. Photoelectric anti-detection and photoelectric interference cover each other in the classification. Special photoelectric anti-detection measures mainly refer to the coding technique. If anti-detection measures are taken before taking antiinterference measures, the enemy may not have the chance to release interference, so anti-detection should precede over anti-interference measures, which plays an important role for the attacking party [11]. Technologies including smoke screens and chaff realize photoelectric antidetection by means of shield. Smoke screen interference is a technological means to interfere photoelectric detection, sighting, and guided weapon systems by releasing a large amount of aerosol particles in the air to change transmission characteristics of EMWs in media. Smoke screen interference has dual functions of stealth and deception. The principle of chaff interference is similar to chaff interference to radars: cloud-like, slowly falling chaff is released in the air to shield signals emitted by targets or scatter optical signals of active detection and guided weapons. The technology also has dual functions of stealth and deception. Technologies such as camouflage, stealth, and decoys mainly work by reducing or imitating the optical signals radiated by targets, so as to reduce the target discovery probability of photoelectric detection systems and detection and identification systems or improving the misjudgment

1.2 Detection on Derived Attributes of Targets

13

rate. For example, IR jammers radiate IR waves with the peak wavelength similar to those of engines or other heating components of targets while extremely high intensity. These IR waves are emitted by optical antennas after multiple amplitude modulations. In this way, two hot targets appear in the field of view of seekers, which enter the tracking loop at the same time after being processed by the chopper wheel, thus inducing the guided missile to deviate from the true target [12]. Destruction and blinding refer to interfere, blind, and even destroy anti-detection of photoelectric devices of the enemy by using high-energy weapons including lasers. When irradiated by intense laser beams, photoelectric sensors will be overloaded and saturated, damaged or thermally decomposed, vaporized, melted, and even destroyed, resulting in failure of equipment and weapons. For example, human will be dizzied or blind if being irradiated by intense laser beams, so that operating personnel may lose combat capability and opportunity [13]. According to the development of modern new technologies and examples of modern high-tech local warfare, it is foreseeable that photoelectric detection will be substantially developed toward high resolution, comprehensiveness, detection and attack integration, and fusion with other detection means. These trends raise new challenges for the development of photoelectric detection. Besides, it is highly necessary and urgent to explore new photoelectric detection measures and systems, which will be the core driver for winning advantages in face of existing anti-detection measures and also a powerful guarantee for promoting the above development trends.

1.2 Detection on Derived Attributes of Targets 1.2.1 Concepts of Derived Attributes of Targets In recent years, anti-detection technologies such as stealth, camouflage, and deception have developed rapidly, forming a batch of high-performance land, marine, air, and spaceborne platforms, which brings the unprecedented challenge to the development of detection technologies. At present, most conventional detection methods directly detection the electromagnetic signals reflected or radiated by targets themselves to estimate the structural forms and motion features of targets, thus determining the attributes of targets. The detection effect is obviously affected by the stealth, deception, camouflage, and locomotion of targets. For example, targets are directly observed or imaged in visible-light detection or low-light detection, which identifies airplanes, panzers, ships and warships based on specific attributes of targets, such as the appearance and shape. The appearance and shape belong to the ontological attributes of targets, which are easily simulated or imitated, and camouflage and decoys exert significant influences on the detection effect, for example, detection devices including laser rangefinder, laser velocimeter, and lidar judge attributes of targets according to motion features such as the range, speed, and orientation of targets. These motion features are also ontological attributes of targets that are easily

14

1 Introduction

influenced by stealth and locomotion of targets. In fact, there are also many attributes other than the ontology of targets, such as disturbances of atmospheric compositions and vibration disturbances, electromagnetic disturbances, and acoustic disturbances induced by moving targets, target discharge, and special parts attached to targets. They are all directly related to attributes of targets. Studying detection technologies of these attributes is of important significance for improving the comprehensive performance, involving the anti-stealth, anti-camouflage, and anti-deception performance of detection devices. Description of attributes of targets is all limited to the detection. Here, the structural forms, motion attributes, and overall electromagnetic characteristics of targets are defined as ontological attributes for target detection. Other than structural forms, motion attributes, and overall electromagnetic characteristics of targets, accessory constituents and incidental attributes of targets that can basically reflect the essential features of targets are defined as derived attributes of targets. From the definition, derived attributes of targets have two core characteristics: ➀ they are new detection attributes. Here, the “new” involves two aspects: one is new incidental attributes of targets, and the other is attributes of targets that are not paid close attention to by conventional detection means while potentially support some emerging detection means. ➁ They reflect some essential features of targets. The “reflection” covers a broad scope. For example, the standard of “reflection” is met as long as some attributes reflect the motion states, structures, and even types of targets. The two characteristics are natures of derived attributes of targets and also judgment standards. According to the standards, many derived attributes of targets can be judged rapidly. Here, two derived attributes of targets, namely wind-field disturbances introduced in Chap. 2 and disturbances of the atmospheric compositional field introduced in Chap. 3, are taken as examples. At first, they belong to new attributes of aerial moving targets; at the same time, features including speed and orientation of targets can be deduced according to the two attributes. For instance, two derived attributes, that is, retroreflectors introduced in Chap. 4 and marks introduced in Chap. 5, are not paid close attention to by conventional photoelectric detection means; however, they are accessory constituents of targets with potential support for the development of new photoelectric detection means; meanwhile, they also reflect features including identifies (friend or foe) and types of targets.

1.2.2 Connotations of Derived Attributes of Targets According to the definition, two basic generation modes of derived attributes can be determined: attached to targets or derived from targets. Target-attached attributes can be described as local significant features of targets themselves, such as retroreflectors or marks of target carriers, engine nozzles of aerial moving targets, radars on warships, and gun barrels of panzers. Target-derived attributes can be described as new attributes generated by targets at work. These include wind-field disturbances or trailing vortexes induced by aerial moving targets, target discharge, electromagnetic

1.2 Detection on Derived Attributes of Targets

15

disturbances, acoustic disturbances, atmospheric disturbances, water-surface disturbances, and gustatory disturbances induced by targets. It is worth noting that for either target-derived or target-attached attributes, only those reflecting essential features of targets in the detection background are termed as derived attributes of targets. Moreover, according to the two standards of the definition, there is another generation mode of derived attributes, that is, feature integration of targets, and the derived attributes generated correspondingly are called synthetic attributes. The theory and application of information fusion, data fusion, and image fusion develop rapidly in recent years, which substantially improves the processing and analysis efficiency and accuracy of signals and data. They stand out in fields such as signal and information processing, pattern recognition, and artificial intelligence and have become a common means of informationalized production. Such fusion and processing mode has been applied to numerous informationalized weapons and equipment, particularly combat platforms of detection and guidance. Data and image fusion in the field of detection produces synthetic attributes. At first, data and image fusion produces new attributes (namely synthetic attributes) that are different from ontological attributes of targets, which meet the standard of “new” for derived attributes. Then, the goal of data and image fusion is to produce signals, features, and even decision weights of higher discernibility and robustness, so as to improve the effects of detection activities including target detection, location, and identification and directly reveal essential attributes of targets. Therefore, these attributes also meet the standard of “reflection” for derived attributes. Hence, although such data and image fusion mode has significant artificial characteristics, it satisfies the definition of derived attributes of targets and the attributes produced thereby are determined as synthetic attributes. In view of this, Chap. 6 introduces an example for photoelectric detection of a specific synthetic attribute, namely the detection and processing of synthetic attributes from airborne/spaceborne integrated images of typical ground and sea-surface targets to further explain key methods to detect this type of derived attributes. The definition of derived attributes of targets is further improved by comprehensively considering characteristics of generation modes of the three derived attributes (attached to targets, derived from targets, and feature integration of targets): other than structural forms, motion attributes, and overall electromagnetic characteristics of targets, the accessory constituents of targets, incidental attributes of targets, and integrated features of targets that all basically reflect the essential features of targets are defined as derived attributes of targets. Although derived attributes differ from ontological attributes of targets to some extent, they in essence both belong to target detection attributes. Therefore, new classification methods can be put forward according to characteristics of derived attributes, or the classification method of ontological attributes can be adopted. The generation modes of derived attributes of targets include those attached to targets, derived from targets, and feature integration of targets, which vividly describe the characteristics of three types of derived attributes. Therefore, derived attributes can be reasonably classified according to the generation modes. Meanwhile, ontological attributes of targets are generally classified based on detection means, which has formed the well-recognized classification methods. When adopting these methods,

16

1 Introduction

Fig. 1.1 Classification of derived attributes of targets

the reasonable classification methods of derived attributes can be obtained. According to the macroscopic detection means, derived attributes can be classified into those for electronic detection, acoustic detection, and photoelectric detection. The category of attributes for electronic detection can be further divided into those for radio signal detection and radar detection. Likewise, the category of attributes for photoelectric detection can be further classified into those for visible-light detection, lowlight detection, laser detection, UV detection, IR detection, and multispectral detection according to the realization principles. Of course, they can be further divided according to the conventional method, while the section only focuses on proposal of reasonable classification methods. In summary, derived attributes of targets can be classified into two basic categories according to generation modes and detection means, and then further divided based on their own characteristics, as shown in Fig. 1.1. On the basis of the classification, the attributes divided according to generation modes and detection means can be combined and then subdivided, taking targetattached attributes for photoelectric detection and target-derived attributes for laser detection for example. This forms more specific and distinctive subclassification.

1.2.3 Typical Photoelectric Detection Methods for Derived Attributes of Targets Photoelectric detection of derived attributes of targets is a novel target detection method of using photoelectric means to detect, identify, track, and surveil targets by obtaining derived attributes including accessory constituents, incidental attributes, or synthetic attributes generated by feature integration of targets that all can reflect essential features of targets. For some derived attributes, such as marks on target carriers, engine nozzles or wake flames of aerial moving targets, and gun barrels of panzers, the well-developed visible-light or IR detection can be used. For some

1.2 Detection on Derived Attributes of Targets

17

derived attributes, such as trailing vortexes and atmospheric disturbances induced by aerial moving targets, retroreflectors on aircraft loading (parking) platforms, airborne/spaceborne integrated hyperspectral synthetic attributes, and smell concentration of warships, new detection technologies and devices should be developed. In addition, some derived attributes, such as wake flows of warships, can be detected using mature acoustic and IR detection or new methods including laser detection of wake bubbles. At present, the research on detection of derived attributes of targets is still in its infancy. Although the same type of derived attributes may be completely different in the detection methods, the conclusive summarization of methods is of less significance. Therefore, the section only briefly presents several typical detection methods for derived attributes of targets.

1.2.3.1

Laser Detection of Wind-Field Disturbances Induced by Aerial Moving Targets

Aerial targets such as airplanes will produce intense atmospheric wind-field disturbances during motion, including wingtip vortexes, jet streams, and boundary layer turbulences. These disturbances are typical target-derived attributes that are characterized by strong disturbance intensity (tens of meters per second), long retention time (as long as tens of and even hundreds of seconds), long spread distance (as far as tens of kilometers), and high detectability. In addition, the ability to acquire high-altitude 3D wind-field information has been greatly enhanced as the long-range, high-precision, and high-resolution wind-field detection technologies such as laser Doppler wind radars are gradually matured. The wind-speed measurement accuracy can reach 1 m/s and the detection range is as long as tens of kilometers. Based on the two advantages, the research team explored lidar detection of aerial moving targets based on wind-field disturbances by combining atmospheric disturbances induced by moving targets and excellent performance of lidars in wind-field detection, aiming at the deficiency of traditional detection means for aerial moving targets. The core is the detection method of global wind-field disturbances [14] and trailing vortexes [15], and the feasibility of laser detection of wind-field disturbances induced by aerial moving targets has been demonstrated.

1.2.3.2

Laser Detection of Atmospheric Compositions Disturbed by Aerial Moving Targets

The large amount of exhaust discharged by aerial targets such as airplanes after combustion of fuels rapidly changes the concentrations of surrounding atmospheric compositions and particularly, the contents of CO2 and water vapor rise significantly. Such disturbances of the atmospheric compositional field belong to the typical targetderived attributes, which provide convenience for detection of aerial moving targets. Characterized by high angular and range resolutions, lidars not only can be used to detect atmospheric wind fields but also are applicable to detection of concentrations

18

1 Introduction

of atmospheric CO2 and water vapor. The general mode of lidar detection of targets is intensive scanning in the detection region for the purpose of finding targets by detecting strong echoes of targets. However, limited by the technical level of lidars, such detection mode generally finds it difficult to detect targets themselves at a high probability in the large scanning airspace, whereas, the disturbances of the atmospheric compositional field induced by targets show a large spatial diffusion range and a long time period of dissipation. This allows target detection by detecting disturbances and diffusion of the atmospheric compositions induced by targets using lidars. Based on the advantage, the research team studied the laser detection means for concentrations of atmospheric CO2 and water vapor and demonstrated the feasibility of laser detection of atmospheric compositions disturbed by aerial moving targets [16].

1.2.3.3

Active Imaging Detection of Retroreflective Attributes of Targets

To improve the safety of nighttime operations, retroreflectors are widely applied to warships and aircraft landing platforms including airports as well as runways, indication signs, and anti-collision signs on motion platforms and cave depots such as garages, hangars, and boathouses. They belong to typical target-attached attributes. By detecting retroreflectors, the detection activities such as detection, identification, and location of the above targets can be realized. Retroreflectors can reflect the incident light within a small solid angle along the incident direction, which strictly distinguishes retroreflectors from the targets to which they are attached and the background. If such feature can be captured by photoelectric means, a novel target detection means that is highly robust for the complex background can be formed. For this purpose, the research team proposed to use active imaging to quantify retroreflective attributes into luminance information that is easily analyzed and processed. In addition, the method has been successfully applied to detection of retroreflectors, demonstrating the feasibility of active imaging detection of retroreflective attributes of targets [17].

1.2.3.4

Passive Imaging Detection of Attributes of Marks on Targets

Visible marks with various meanings are generally present on targets such as airplanes and vehicles, including all kinds of alphanumeric numbers and patterns, which are important constituents of target carriers. These marks are attached to target carriers and therefore belong to typical target-attached attributes. Identification of these marks not only assists target detection but also provides support for detection activities including target identification, tracking, and comprehensive analysis. Marks and patterns change with the motion state, shooting angle, and illumination of the carrying platforms and have large dynamic ranges in aspects including the noise, scale, and affine transformation. For successful detection and identification, high requirements

1.2 Detection on Derived Attributes of Targets

19

have been set for the contrast of marks and patterns, as well as for the robustness of algorithms in the noise, illumination variation, and affine transformation. In view of this, the research team, on the one hand, investigated the efficiency of graying of color-discrete characteristic in improving the contrast of marks and patterns [18]; on the other hand, a highly robust mark identification method was studied based on the stability of SIFT operator in noise and illumination variation and the invariance of SIFT operator for affine transformation [19], demonstrating the feasibility of passive imaging detection of marks on targets.

1.2.3.5

Target Detection Based on Synthetic Attributes in Airborne/ Spaceborne Integrated Hyperspectral Images

Target detection in modern IT-based warfare is increasingly challenged by the large range of motion, wide distribution range, and strong stealth of targets. Particularly in long-distance, wide-range, and precision detection, higher requirements have been set for the target discovery, identification, location, and detection. The fusion of multiple detection platforms and multiple detection data is becoming the development trend for target detection and information processing. These fused data belong to the synthetic attributes that are concerned in the monograph. Spaceborne platforms are characterized by the wide detection range and high efficiency; detection data of airborne platforms are complete and have high precision; hyperspectral imaging detection has characteristics including the wide spectral coverage and high accuracy. Based on these characteristics, the research team came up with a detection and processing method applicable to the fusion of airborne/spaceborne hyperspectral images. The method realizes the wide-range, high-precision, and high-efficiency target detection dominated by space detection and effectively improves the precision, speed, efficiency, and intelligence of airborne/spaceborne hyperspectral detection. The feasibility of target detection based on synthetic attributes in airborne/ spaceborne integrated hyperspectral images also has been demonstrated [20].

1.2.3.6

Detection of Smell Information of Warships

During navigation of a warship, wakes with obvious oil contamination are formed behind. Such wakes contain ample smell information of the warship and form a special smell of wakes. The smell of wakes belongs to a typical target-derived attribute and generally comes from two sources: one is caused by the hull of warships. For example, the protective oil for corrosion resistance, additional coatings for adsorbing or attenuating radar waves, silent tiles on the hull surface of dunkers, and biological and abiotic attachments on warships due to long-term immersion in water are all odor sources of warships. The other type of smells comes from the interior of warships. For instance, the machine oil leaked to water during work of internal machinery of warships and special materials such as coolant water discharged during work of reactors in nuclear-powered submarines enhance the smell features of wakes of

20

1 Introduction

warships. Moreover, wakes of warships can remain for a rather long time period at a considerable scale in water, which makes it possible to detect warships according to the smell of wakes. In fact, the smell information of warships not only exists in wakes, but also the smell of warships is filled in water surrounding warships, including the water ahead of the head and in both sides of the board. That is, the warship is surrounded by the strong smell of itself. When the smell is dissolved in water, the corresponding water also has a special taste. The information of warships can be deduced by acquiring taste information of water and then inverting the smell information dissolved in water, so as to detect target warships. Modern sensors have been able to test weak smell information, which makes the detection of warships based on the smell information possible [21].

1.2.3.7

Laser Detection of Smells or Wake Bubbles of Warships

Warships may disturb seawater when they move in the ocean. The cavitation of propellers, breakage of waves on sea-surface, and entrainment of a large amount of air at the waterline of warships all produce wakes behind the warships. Wakes belong to target-derived attributes, which provide convenience for detection of warship targets. The bubble cluster in the wakes is composed of bubbles with different diameters and the bubble density changes with depth and time. Due to seawater pressure and gas diffusion from bubbles, large bubbles rapidly rise to the surface at a fast rate and then burst, while small bubbles can remain for a long time, which are mainly smallscale and low-density bubble clusters smaller than 160 µm. These bubble clusters contribute as much as 10% to the backscattering of seawater, so the difference of propagation characteristics of light waves in wakes and in seawater can be used as a basis for detecting wakes. The high sensitivity of lidars provides conditions for the high-precision detection of wakes, so various nations have made lots of efforts in research on laser wake homing [22].

References 1. Zeng HF. Modern reconnaissance and surveillance technology. Beijing: National Defense Industry Press; 1999. 2. Liu ZC. Equipment and development of optoelectronic reconnaissance and warning technology. Laser Infrared. 2008;38(7):629–32. 3. Lei L. Reconnaissance and surveillance—the eye and ear of the battle space. Beijing: National Defense Industry Press; 2008. 4. Chen FS. Strategic thinking on the development of military optoelectronic system technology. Ship Sci Technol. 2005;27(4):5–8. 5. Li CM, Chen QX. Research on the application of airborne optoelectronic reconnaissance system. Aerosp Electron Countermeasures. 2007;2:25–7. 6. Ai KC. Progress and prospects of low light night vision technology. Appl Opt. 2006;27(4):303–7.

References

21

7. Ma R. Analysis of the development of laser guided weapons. Infrared Laser Eng. 2008;37(s3):266–70. 8. Hu YH. Laser imaging target reconnaissance. Beijing: National Defense Industry Press; 2013. 9. Zhao J. The status and development trends of infrared detection technology. Ship Electron Eng. 2007;27(1):32–6. 10. Rockwell DL. Targeting, navigation, and ISR system converge. J Electron Def. 2005;28(10):42– 50. 11. Liu ST, Gao DH. Optoelectronic countermeasures technology and its development. Optoelectron Technol Appl. 2012;27(6):1–9. 12. Hu YH. Preliminary exploration of directional interference technology for infrared imaging. J Coll Electron Eng. 2001;20(1):18–21. 13. Hu YH, Jiang H. Satellite imaging reconnaissance and countermeasures. Acad J Electron Countermeasures. 2003;4:30–2. 14. Wu YH, Hu YH, Gu YL, et al. Research on a new algorithm for obtaining air moving target information. J Opt. 2010;30(s1):9–13. 15. Wu YH, Hu YH, Dai DC, et al. Based on 1.5 µ research on aircraft wake vortex detection technology using M-doppler lidar. Acta Photonica Sinica. 2011;40(6):811–7. 16. Gao P, Hu YH, Zhao NX, et al. Accuracy analysis of atmospheric component detection using all fiber differential absorption lidar. J Opt. 2013;34(3). 0301003-1-0301003-5. 17. Yang X, Lv DL, Hu YH, et al. A collision warning method for retroreflector laser detection. ZL201610380057.8 [P]. 2016-06-01. 18. Yang X, Ling YS, Li S, et al. Graying for images with color-discrete characteristic. Int J Light Electron Opt. 2011;122(18):1633–7. 19. Chen HL, Hu B, Yang X, et al. Chinese character recognition for LPR application. Int J Light Electron Opt. 2014;125(18):5295–302. 20. Chen SJ, Hu YH, Xu SL, et al. The k-nearest-neighbor simplex based on adaptive C-mutual proportion standard deviation metric for target clustering of hyperspectral remote sensing imagery. J Appl Remote Sens. 2014;8(1). 083578-1-083578-18 21. Yu Y, Wang JA, Ma ZG, et al. A new method for detecting ships. Ship Sci Technol. 2009;31(5):49–51. 22. Wang XW, Zhou TH, Chen WB. Research on laser backscattering characteristics of ship wake. J Opt. 2010;30(1):14–8.

Chapter 2

Laser Detection on Disturbance of Wind-Field of Air Moving Targets

How to effectively detect low-detectable aerial targets is a major difficulty that needs prompt solution in the field of modern air-defense early-warning. Because most of such targets apply compound stealth technologies and their stealth band is broadened, the detectability of targets has been substantially reduced. Generally, changes in surrounding environmental characteristics induced by motion of aerial targets mainly include electric discharge, ionization, electromagnetic and atmospheric disturbances, and so on. Therein, atmospheric disturbances mainly include disturbances of the atmospheric compositional field, temperature field, and wind field. Wind-field disturbances refer to changes in the surrounding atmospheric wind field caused by flight of aerial targets, including overall wind-field disturbances and trailing vortexes. For aircraft, trailing vortexes caused by their wings in both sides are very fierce and show strong spreading characteristics. In addition, the trailing vortexes also have extremely important multi-feature associations with the aircraft. Due to the high brightness, high degree of collimation, and short pulses of lasers, lidars have become the optimal means to acquire information of atmospheric wind fields in the remote, at high precision, and of high resolution. They can also reach the goal of finding and identifying aerial moving targets indirectly by detecting overall wind-field disturbances or trailing vortexes. The chapter focuses on the elaboration of characteristics of atmospheric wind fields and acquisition of disturbance information of targets, detection system for target-induced atmospheric wind-field disturbances, characteristics of aircraft trailing vortexes, and laser detection and identification.

2.1 Characteristics of Atmospheric Wind Fields and Acquisition of Disturbance Information of Targets Based on characteristics of background atmospheric wind fields, a lidar can be used to detect disturbances of the atmospheric wind field caused by flight of aerial targets to acquire information of aerial moving targets. The section introduces characteristics © National Defense Industry Press 2023 X. Yang and Y. Hu, Photoelectric Detection on Derived Attributes of Targets, https://doi.org/10.1007/978-981-99-4157-5_2

23

24

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

of atmospheric wind fields, provides the basic principle and main process to acquire information of moving targets based on wind-field disturbances, and verifies that the novel target detection method is feasible. This provides a new approach to the efficient detection and discovery of aerial moving targets in the future [1].

2.1.1 Characteristics of Atmospheric Wind Fields Winds, as a result of air circulation in the nature, are formed due to rotation of the Earth and the pressure difference caused by different temperatures in various regions on the Earth. Winds are characterized by volatility and uncontrollability. Despite the volatility of wind velocity, changes and distribution of the wind velocity also follow some rules. Though seemingly random, winds in the nature also follow a certain statistical law in fact [2]. The haste motion of airflows in the atmosphere is called turbulences, in which air bumps are one of the turbulences. Wind shear, a kind of turbulence that is more hazardous than air bumps, is a local atmospheric phenomenon resulting from the abrupt changes in the wind velocity and direction. Clear-air turbulences, as another form of turbulences, appear in a high altitude in fine weather. It is a violent airflow disturbance that occurs without signs. Despite great perniciousness, wind shear and clear-air turbulences occur with only a very low probability above the boundary layer. The atmospheric wind velocity in a small scale and a short period of time can be decomposed into the sum of the average wind velocity and the pulsating wind velocity. The average wind velocity refers to a quantity that remains unchanged with time in a period of time, while the pulsating wind velocity is a quantity that changes randomly with space and time. The average wind velocity changes in the following trend with the altitude: it increases with the altitude below the tropopause (9 ~ 12 km), reaches the maximum at the tropopause (12 km), reduces with the altitude afterwards and lowers to the minimum at 20 ~ 25 km, and then gradually increases again at higher altitudes. At the gradient altitude z G , the average wind velocity at the altitude h is expressed as follows according to the energy law [3]: { vh =

( h )α v 10 10 h ≤ zG ( z G )0.19 h > zG v 10 10

(2.1)

where v 10 represents the surface wind velocity at 10 m; the exponent α is related to the surface roughness parameter z 0 . . For a wide grassland (similar to most airports), when z 0 = 0.1 while α = 0.19, the gradient altitude can be expressed as follows: z G = 1000z 00.19 = 660.7 m

(2.2)

Therefore, the background atmospheric wind field is distributed continuously and stably in the high altitude and the random pulses are low when eliminating influences

2.1 Characteristics of Atmospheric Wind Fields and Acquisition …

25

Fig. 2.1 Monthly average wind velocities at Hefei in recent 30 years

of low-probability events including wind shear and clear-air turbulences. Moreover, because the troposphere is heavily affected by the surface relief, humidness, and temperature, the temperature near the tropopause may change abruptly with the rising altitude, which greatly blocks the vertical atmospheric motion, so that the wind velocity in the troposphere is not constant. Data published on Hefei Meteorological Website (Fig. 2.1 shows the monthly statistical averages of surface wind velocities from 1971 to 2000; Fig. 2.2 displays the statistics of frequencies of each wind direction in each month from 1971 to 2000) [4] were analyzed. The analysis reveals that the average wind velocity of Hefei in recent 30 years was in the range of 1.6 ~ 3.3 m/s, which was distributed stably on the whole while the frequency of each wind direction in each month fluctuated greatly. This means that the atmospheric wind field shows disturbances such as clear-air turbulences and wind shear and the average atmospheric wind velocity varies with the increasing altitude. Taking Hefei region as an example, the wind direction changes obviously over seasons.

2.1.2 Principle of the Information Acquisition Algorithm of Moving Targets The basic principle of acquiring information of moving targets based on laser detection of wind-field disturbances is shown in Fig. 2.3 [1], mainly including three parts, namely, wind-field modeling, wind-field detection, and moving target detection. Therein, wind-field modeling is mainly to acquire the movement and change regularity of the atmospheric wind field through long-term observation and monitoring of background atmospheric conditions of a certain area and statistical research of the wind-field distribution data. On this basis, the background wind field is modeled

26

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

Fig. 2.2 Statistics of frequencies of each wind direction in each month at Hefei in recent 30 years

and the background wind-field database is built. Wind-field detection is to scan s specific airspace using a lidar system and invert the radial and horizontal wind velocity components by receiving the reflected laser echo signals, so as to attain the wind-field distribution on the scanning sector. Moving target detection obtains distribution of disturbed wind fields in the scanned airspace by filtering influences of the background wind field on the scanned wind field; then based on the disturbance detection principle, information including the disturbance location, velocity magnitude, and shape is extracted. Combining with the disturbance information, one can judge whether the disturbance is caused by a moving target or not. If not, there is no moving target in the scanned airspace; if yes, a moving target may be present. Furthermore, information including the possible location and general attributes of the moving target can be acquired based on the internal correlation between the disturbance and the moving target.

2.1.3 Inversion Algorithm for Distribution of the Atmospheric Wind Field 2.1.3.1

Inversion of the Radial Wind Velocity in the Wind Fields

Detection of the atmospheric wind velocity using a Doppler lidar is realized by measuring the Doppler shift of atmospheric molecules. After a telescope receives echo signals of laser pulses from atmospheric molecules, detection of the Doppler shift of atmospheric echo signals is turned into detection of signal intensity using an iodine molecular filter. That is, tiny frequency changes are transformed to strong changes in signal intensity, thus carrying out incoherent pulsed laser Doppler velocimetry [5]. The radial wind velocity vwr and Doppler shift Δf d have the following relationship:

2.1 Characteristics of Atmospheric Wind Fields and Acquisition …

27

Fig. 2.3 Flowchart of information acquisition of moving targets based on laser detection of windfield disturbances

Δ fd =

2vwr cos θ λ

(2.3)

where vwr represents the radial wind velocity; Δf d denotes the Doppler shift, the value of which is the frequency difference between the frequency of laser pulsed Doppler echo signals and the emission frequency of the lidar; λ refers to the emission wavelength of the lidar. The principle of measuring Doppler shift using an iodine molecular filter is illustrated in Fig. 2.4. If the backscattering spectral distribution of the atmosphere is h( f ) and the spectral distribution at the edge of an absorption line of the selected iodine absorption filter is F( f ), then the transmissivity function T ( f ) in Fig. 2.4 is the convolution of h( f ) and F( f ), that is

28

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

Fig. 2.4 Schematic diagram for detection of Doppler shift by using an iodine molecular filter

{+∞ ) ( ) ( T( f ) = h f, F f − f, df,

(2.4)

−∞

According to the equation of the lidar, supposing that the photon number emitted by the pulse laser is N0 , then the photon numbers N1 and N2 received by photoelectric detectors 1 and 2 are separately ⎧ ⎫ ⎨ {r ⎬ N A N1 (r ) = η1 2 Δr [βa (r ) + βm (r )] × exp −2 [αa (r , ) + αm (r , )]dr , (2.5) ⎩ ⎭ 2 r 0

N2 (r, f ) =

N A η2 Δr [βa (r )Fa ( f ) + βm (r )Fm ( f )] 2 r2 ⎧ ⎫ ⎨ {r ⎬ × exp −2 [αa (r , ) + αm (r , )]dr , ⎩ ⎭

(2.6)

0

where η1 and η2 separately represent the products of optical efficiencies of two channels with the quantum efficiency of photoelectric detectors; A denotes the receiving area of the lidar system; Δr refers to the resolution of detection range of the lidar system; r is the detection range; βa and βm represent the backscattering coefficients separately of atmospheric aerosols and atmospheric molecules; αa and αm denote the extinction coefficients separately of atmospheric aerosols and atmospheric molecules.

2.1 Characteristics of Atmospheric Wind Fields and Acquisition …

29

The following is defined in Eq. (2.6): {+∞ Fa ( f ) = T ( f , )h a ( f − f , )d f ,

(2.7)

−∞

fm( f ) =

{+∞ T ( f , )h m ( f − f , )d f ,

(2.8)

−∞

where h a and h m separately denote the backscattering spectral distribution of atmospheric aerosols and atmospheric molecules. That is, Fa ( f ) and Fm ( f ) separately represent the spectral responses of backscattering of atmospheric aerosols and atmospheric molecules through the iodine absorption filter. By using Eqs. (2.5) and (2.6), the spectral transmissivity of the iodine absorption filter can be obtained as T (r, f ) =

βa (r )Fa ( f ) + βm (r )Fm ( f ) N2 (r, f ) = K1 N1 (r ) βa (r ) + βm (r )

(2.9)

where K 1 is the system correction parameter. The atmospheric backscattering ratio Rb (r ) is generally defined as Rb (r ) =

βa (r ) + βm (r ) βm (r )

(2.10)

By substituting Eq. (2.10) into (2.9), there is T (r, f ) = K 1

[Rb (r ) − 1]Fa ( f ) + Fm ( f ) Rb (r )

(2.11)

When the laser emission frequency is f o , the corresponding spectral transmissivity of the iodine absorption filter is T (r, f o ). Because of atmospheric oscillation, the backscattered signals of atmospheric aerosols and atmospheric molecules will produce Doppler shift Δ f d relative to the laser emission frequency f o , which corresponds to the spectral transmissivity T (r, f o + Δ f d ) of the iodine absorption filter. By expanding T (r, f o + Δ f d ) to Taylor series at f o and ignoring the higher-order derivative terms, the following is obtained: dT (r, f o ) K1 Δ f ≈ T (r, f o ) + T (r, f o +Δ f d ) + df Rb (r ) } { dFa ( f ) dFm ( f ) + Δf × [Rb (r ) − 1] df df

(2.12)

30

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

It can be obtained from Eq. (2.12) that Δ fd ≈

T (r, f o + Δ f d ) − T (r, f o ) { } [Rb (r ) − 1] dFda (f f ) + dFdmf( f )

K1 Rb (r )

(2.13)

In this way, the radial wind velocity can be inverted. vwr =

2.1.3.2

c c Δ fo ≈ × 2 fo 2 fo

T (r, f o + Δ f d ) − T (r, f o ) { } [Rb (r ) − 1] dFda (f f ) + dFdm f( f )

K1 Rb (r )

(2.14)

Inversion of the Horizontal Wind Velocity in the Wind Field

Many inversion methods of horizontal wind fields are available, mainly including velocity azimuth display (VAD), volume velocity processing (VVP), and velocity azimuth processing (VAP) [6]. Attributed to the simple calculation, small calculation amount, easy implementation on the computer, and reliable inversion results, VAP is a convenient method to invert the wind field based on a single Doppler radar. Here, the VAP method is used to invert the horizontal wind field. The method assumes that the angular velocities of adjacent orientations are equal, that is, local uniform, then the wind direction and velocity can be calculated according to the Doppler velocity profiles distributed on each range ring with the azimuth. The principle of VAP is shown in Fig. 2.5. It is stipulated that vwr > 0 and vwr < 0 separately indicate that winds approach to and depart from the radar. vwr1 and vwr2 separately represent the adjacent horizontal Fig. 2.5 Schematic diagram for the principle of VAP

2.1 Characteristics of Atmospheric Wind Fields and Acquisition …

31

and radial velocities at an inversion point; α denotes the included angle of the horizontal and radial velocity with the wind vector; θ is the azimuth. When the pitching angle of scanning is low, the vertical falling velocity can be ignored. In the case, the horizontal wind velocity can be calculated based on Doppler velocity profiles on the range ring that are distributed with the azimuth. The horizontal wind velocity is | | | vwr1 − vwr2 | | | vw = | 2 sin α sin Δθ |

(2.15)

The horizontal wind direction is tan α = −

vwr1 − vwr2 cot Δθ = A vwr1 + vwr2

(2.16)

The value of α is calculated as ⎧ α ⎪ ⎪ ⎨ α ⎪ α ⎪ ⎩ α

= arctan = arctan = arctan = arctan

A, vwr1 − vwr2 A + π, vwr1 − vwr2 A, vwr1 − vwr2 A − π, vwr1 − vwr2

> 0, > 0, < 0, < 0,

vwr1 + vwr2 vwr1 + vwr2 vwr1 + vwr2 vwr1 + vwr2

>0 0 136,000 kg

2.3 Generation Mechanisms and Characteristics of the Disturbance Field …

47

Fig. 2.15 Schematic diagram for rotation of left and right trailing vortexes generated by aircraft wings

vvtmax is found at the /location where the vortex core radius is rc . The tangential velocity decreases as 1 r with the growing range from the vortex core center (larger r ). Moreover, the vortex core radius rc enlarges while the maximum velocity vvtmax lowers with time after rolling up of the wingtip trailing vortexes. Changes of the two with time reflect characteristics in the four change stages of trailing vortexes (generation stage, stabilization stage, weakening stage, and vanishment stage). 3) Descent velocity v0 of trailing vortexes Descent velocity v0 refers to the velocity of downward motion of trailing vortexes after departing from the aircraft and is caused by the induction between trailing vortexes. The analysis of Eq. (2.39) shows that the stronger the trailing vortexes are, the faster the downward drift. The descent velocity of trailing vortexes of large aircraft is much faster than that of small one. Flight experiments show that [12] the descent velocity of trailing vortexes gradually reduces under the windless condition and finally trailing vortexes suspend in the midair and do not descend any longer. 4) Decay rate ξ of trailing vortexes Due to the large outward rotational velocity, trailing vortexes drive surrounding viscous air to rotate together, so that energy is constantly consumed and trailing vortexes gradually decay and finally vanish. Generally, the strength of turbulent flows in surrounding air and the temperature can influence the energy exchange

48

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

of trailing vortexes with surrounding air: they promote or delay the vanishment of trailing vortexes. Under the same atmospheric conditions, the stronger the trailing vortexes are, the more slowly the vortexes are dissipated and the longer the survival time; and vice versa. Moreover, surface friction and wind velocity both affect the life span of trailing vortexes.

2.3.4 Simulation Analysis By combining with the analytical model of the disturbance field of trailing vortexes, software Matlab is used to calculate characteristic parameters of trailing vortexes of civil and military aircraft separately. Meanwhile, the induced velocity field of trailing vortexes is calculated using the Fluent platform to analyze the space–time spreading characteristic of trailing vortexes. Moreover, taking the typical airbus A340 as an example, characteristic parameters of wind-field disturbances induced by trailing vortexes of airbus A340 under typical flight conditions are calculated based on Matlab. Table 2.3 displays simulation parameters for trailing vortexes of airbus A340 under typical flight conditions, in which input parameters for model simulation are listed. By inputting the aircraft type parameters of A340, flight parameters, and environmental parameters, the characteristic parameters of trailing vortexes of airbus A340 under typical flight conditions are calculated and output, as listed in Table 2.4. Figure 2.16 illustrates the isogram of the tangential velocity on the profile of the trailing vortexes of A340 that are just formed and it is output by simulation. Colors of each point in the figure correspond to the velocities of corresponding points, which intuitively displays the strength distribution of trailing vortexes on the vertical profile. Figure 2.17 shows the distribution curves of the tangential velocity on the profile of the trailing vortexes that are just formed and they are output by simulation, which reflect the characteristic that the interaction between left and right trailing vortexes causes the velocity component of trailing vortexes to change. Table 2.3 Simulation parameters for trailing vortexes of airbus A340 under typical flight conditions Types of parameters

Parameters

Values

Aircraft type parameters

Type

Airbus A340

Weight m/kg

368,000

Flight parameters

Wingspan l ws /m

63.45

Flight altitude H/km

1

Flight velocity vp /m·s−1 Environmental parameters

Air density in the airspace

100 ρ/kg·m−3

Local gravitational acceleration g/m2 ·s−1

1.16 9.81

2.3 Generation Mechanisms and Characteristics of the Disturbance Field …

49

Table 2.4 Characteristic parameters of trailing vortexes of airbus A340 under typical flight conditions Types of parameters

Parameters

Characteristic parameters of trailing vortexes

Distance from vortex core centers b0 /m

49.808

Vortex circulation at the root G0 /m2 ·s−1

624.507

Vortex core radius r c /m

Maximum tangential velocity vvtmax / m2 ·s−1 Fig. 2.16 Isogram of the tangential velocity on the profile of trailing vortexes of A340

Fig. 2.17 Distribution curves of the tangential velocity on the profile of trailing vortexes of A340

Values 2.591

19.178

50

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

The analysis of Fig. 2.18 reveals that velocity distribution on the profile of aircraft trailing vortexes shows obvious symmetry. The closer the location to the vortex cores, the larger the velocity, and the denser the lines of equal velocity; at locations distant from the vortex cores, the velocity is lower and the influences of vortex cores are basically ignorable on the whole in the distant. Figure 2.17 reveals that distribution curves of the tangential velocity of the overall two vortexes obtained by considering the interaction between left and right vortexes differ greatly from the velocity curves attained through calculation of left and right vortexes separately. It can be seen that the maximum velocity along the vortex core radius has changed and especially the velocity in the area between two vortex cores changes more greatly due to interaction of the two. Combining with the analytical model of trailing vortexes, the characteristic parameters of trailing vortexes of A340 have been calculated above using Matlab, mainly 100

60

1

40

1

4

0

2

12 15

1

8 18 12 12

8

1

5 2

1

2

-20

1

1

1 -40

-60 -50

0

-50

50

(a) t=0.0 s, s=0.0 km

200

2

180

1 4

1 2

2 1

1 0

2

140

2

120 100

2 80

-20 -40

160

5

40 20

1

Z(m)

Z(m)

60

50

(b) t=10.0 s, s=1.0 km

0000

1 100

0

Y(m)

Y(m)

80

1

4 2

45

0

2

-40

2 6

20

4

-20

2

40

Z(m)

1

1

60

2

4

20

Z(m)

80

2

60 -50

0

50

Y(m)

(c) t=30.0 s, s=3.0 km

-50

0

50

Y(m)

(d) t=100.0 s, s=10.0 km

Fig. 2.18 Isograms of velocity distribution on the profile of trailing vortex cores of A340

2.3 Generation Mechanisms and Characteristics of the Disturbance Field …

51

at the moment that the trailing vortexes are just rolled up. Here, Fluent is used for further programming and calculation and particularly, simulation of the spread and decay characteristics of disturbances caused by aircraft trailing vortexes with time and distance is paid more attention to. Figure 2.18 illustrates the velocity isotherms on the profile of trailing vortex cores of A340 at different moments (t = 0.0 s, 10.0 s, 30.0 s, 100.0 s, which also correspond to different flying distances of the aircraft s = 0 m, 1 km, 3 km, 10 km). By analyzing simulation results of wind-field disturbances caused by trailing vortexes of airbus A340 under typical flight conditions, it is preliminarily determined that wind-field disturbances caused by aerial targets have the following significant characteristics (more details are listed in Table 2.5): (1) Intense disturbances. Research has shown that the high-altitude background atmospheric wind field is distributed continuously and stably, with small random fluctuations, and the disturbances are generally at the wind scale of breezes (vw = 1.6 ∼ 3.3 m/s). In comparison, the velocity magnitude of wind-field disturbances caused by trailing vortexes of aircraft targets in the air is much higher than the wind scale of background wind fields. Through simulation of trailing vortexes of airbus A340, the maximum vortex velocity when trailing vortexes of A340 are just rolled up is vvtmax = 19.178m/ s and the vortex circulation is Γ0 = 624.507 m2 /s. In addition, the wind-field disturbance caused by trailing vortexes is more intense if the aircraft weight m is larger or the flight velocity vp is lower. (2) Long durations. Simulation reveals that disturbances caused by trailing vortexes of airbus A340 gradually decay and completely vanish at 100 s, when they begin to merge with the surrounding background atmospheric wind field. Moreover, the photography experiments on vanishment of trailing vortexes of B747 conducted in other countries also show that [7] trailing vortexes begin to substantially spread at 90 s and completely disappear after 130 s. (3) A long spreading distance. The analysis indicates that the trailing vortexes of A340 can survive for 100 s. If it flies at 100 m/s, the spreading distance of the wind-field disturbance caused by trailing vortexes can reach 10 km. Besides, foreign research finds that [21] the strength of trailing vortexes 50 ~ 500 m behind some transonic aircraft does not weaken obviously; the strength weakens Table 2.5 Characteristics of wind-field disturbances caused by trailing vortexes of the aerial aircraft target Types of parameters

Parameters

Parameters and units

Values

Wind-field disturbances

Velocity magnitude

vvtmax /m·s−1

About 20 m/s

G0

/m2 ·s−1

Above 300 m2 /s

Duration

T s /s

Longer than 100 s

Spreading distance

Ds /km

A dozen of kilometers

Transverse range

L s /m

Wider than 100 m

52

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

by one half only when the distance reaches 1500 ~ 2000 m; the trailing vortexes completely vanish only when the distance is a dozen of kilometers or longer. (4) A wide transverse range. By analyzing simulation results of A340, the windfield disturbances caused by trailing vortexes also spread obviously on the transverse section. The diameter of the round spreading section has reached 120 m and above (equivalent to two times of the wingspan). Therefore, the transverse spreading range of the wind-field disturbance caused by trailing vortexes is far larger than the cross-sectional area of the aircraft target itself from the perspective of transverse section.

2.4 Detection of Trailing Vortexes Based on the Coherent Doppler Lidar The wind-field disturbance caused by aerial targets has been quantitatively studied above through modeling and simulation. The analysis reveals that the wind-field disturbance has characteristics including the large velocity magnitude, long duration, long spreading distance, and wide transverse range that facilitate detection. At present, the atmospheric wind-field detection technique of long distance, high precision, and high resolution using the existing Doppler wind lidar has been gradually matured and widely applied. Comprehensively considering that coherent detection has advantages including higher detection SNR and range than incoherent detection, the 1.5-μm pulsed coherent Doppler lidar was used to detect wind-field disturbances caused by aerial targets, that is, the disturbance field caused by trailing vortexes. By doing so, the research attempts to rapidly and remotely acquire high-precision data distribution and characteristic information of wind-field disturbances.

2.4.1 Coherent Doppler Laser Detection Principle of Aircraft Trailing Vortexes Figure 2.19 shows the principle of coherent Doppler laser detection of the windfield disturbances caused by aerial targets (disturbance field of trailing vortexes). Therein, the ground-based 1.5-μm pulsed coherent Doppler lidar emits laser pulses at a certain repetition frequency and uninterruptedly scans the atmospheric wind field on the plane vertical to the flight direction of the target with a certain pitching angle. Then, it inverts the velocity distribution of each point on the scanning sector by receiving and processing laser echo signals backscattered by atmospheric molecules or aerosol particles. This is followed by extraction of disturbance information of trailing vortexes on the scanning sector using the identification algorithm of windfield disturbances and calculation algorithm of the disturbance field of trailing vortexes, so as to fulfill laser detection of the disturbance field of target trailing vortexes. In Fig. 2.19, the positive direction of the X axis is defined as the reference

2.4 Detection of Trailing Vortexes Based on the Coherent Doppler Lidar

53

Fig. 2.19 Schematic diagram for the principle of coherent Doppler laser detection of the disturbance field caused by trailing vortexes

direction, along which the aircraft flies; Z direction is the longitudinal direction of the section vertical to the reference direction; Y direction is transverse direction of the section vertical to the reference direction. The YOZ plane and countless planes parallel thereto constitute the section slices of trailing vortexes that the aircraft flies for different distances [11]. In the detected airspace, the backscattered signals of the atmosphere cause Doppler broadening due to the thermal motion of atmospheric molecules and Brownian motion of aerosol particles, while the overall average motion velocity of particles induces the Doppler shift of atmospheric echo signals. By calculating the Doppler shifts of atmospheric echo signals at different points, the radial velocities of atmospheric molecules or aerosol particles at these points along the laser beam are inverted. Supposing that radial velocity of atmospheric molecules or aerosol particles along the laser beam is vr and the single-frequency laser with the original frequency of f 0 undergoes Doppler shift Δ f d to change to frequency f s = f 0 + Δ f d after scattering, then the Doppler frequency Δ f d can be expressed as follows: Δ f d = 2 f 0 vr /c =

2 vr λ0

(2.47)

where c represents the light velocity; λ0 is the laser wavelength corresponding to frequency f 0 .

54

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

Fig. 2.20 Block diagram for the flowchart of coherent Doppler laser detection

Moreover, the measurement of Doppler shift mainly relies on the coherent (heterodyne) detection method. The flowchart of coherent Doppler laser detection is displayed in Fig. 2.20 [12]. Generally, some laser beams emitted by the lidar are taken as the reference signals, that is, the LO beam, the frequency of which is the emission frequency f 0 of the lidar. It is supposed that the frequency of backscattered signals of atmospheric molecules or aerosol particles received by the pulsed coherent Doppler lidar is f S . Then the backscattered light of atmospheric molecules or aerosols is projected to the surface of the photoelectric detectors (photomixer) together with LO beams to produce coherent stack (mixing), and radio frequency signals and direct current components with the difference frequency of f S − f 0 are output. After the signals pass the IF amplifier and frequency detector, the Doppler shift Δ f d = f s − f 0 needed is obtained finally. Afterwards, the radial velocity of trailing vortexes along the laser beam is inverted.

2.4.2 Modeling and Analysis of Doppler Spectra for Echoes from Trailing Vortexes 2.4.2.1

Distribution Characteristics of Radial Velocity of Trailing Vortexes

To establish the Doppler spectral model for the echoes from trailing vortexes, the distribution of Doppler velocity (radial velocity) of trailing vortexes can be determined at first. Here, distribution characteristics of the radial velocity of trailing vortexes in the transverse detection mode of the laser (range-height indicator (RHI)) are analyzed. Figure 2.21 illustrates the relationship between the radial velocity and the tangential velocity of trailing vortexes. In Fig. 2.20, the target aircraft flies along the positive direction of the x axis and the ground-based lidar is used for sector scanning of a pair of trailing vortexes generated by left and right wings of the aircraft in flight on the y Oz plane. It is supposed that the cores of left and right trailing vortexes are separately O1 and O2 , and their radial ranges from the lidar are RO1 and RO2 . The scanning pitching angles are αO1 and αO2 . Taking any point O on the right trailing vortex as the research object, its radial

2.4 Detection of Trailing Vortexes Based on the Coherent Doppler Lidar

55

Fig. 2.21 Relationship between the radial velocity and the tangential velocity of trailing vortexes in the RHI mode

range from the lidar is RO and the scanning pitching angle is αO . In addition, the trailing vortex at the point has a tangential velocity vvt (r ) vertical to the vortex core radius and its projection in the scanning direction of the lidar is the radial velocity vvt (r, θ ) at the point. Therein, r represents the range from point O to the vortex core center O2 and θ is the included angle between the radius direction of the point on the section of the trailing vortex and the positive direction of y axis. By analyzing the angular relationship between various velocities in the figure, the included angle γ between directions of the tangential velocity vvt (r ) and radial direction vvt (r ) at point O on the section of the trailing vortex is expressed as follows: γ = 3π/2 + α O − θ

(2.48)

The radial velocity vvr (r ) and tangential velocity vvt (r ) of trailing vortexes at an arbitrary point O have the following relationship: vvr (r, θ ) = vvt (r ) cos γ = vvt (r ) cos(3π/2 + α O − θ ) = vvt (r ) sin(α O − θ ) (2.49) Based on the Hallock-Burnham model for the tangential velocity of trailing vortexes provided in Sect. 2.3.2, the radial velocity distribution on the transverse section of trailing vortexes can be obtained.

56

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

vvr (r, θ ) =

r2 Γ0 sin(α O − θ ) 2πr r 2 + rc2

(2.50)

Therefore, the equation of the curve with the same radial velocity vvr (r, θ ) on the transverse section of trailing vortexes can be obtained through backstepping. r2 −

Γ0 sin(α O − θ )r + rc2 = 0 2πvvr (r, θ )

(2.51)

By solving the above quadratic equation of one variable, the relationship between r and θ can be attained. ] 21 [ Γ02 Γ0 sin(αO − θ ) 1 2 2 + sin (α O − θ ) − 4rc r (θ ) = 2 (r, θ ) 4πvvr (r, θ ) 2 4π2 vvr

(2.52)

] 21 [ Γ02 Γ0 sin(αO − θ ) 1 2 2 − sin (αO − θ ) − 4rc r (θ ) = 2 (r, θ ) 4πvvr (r, θ ) 2 4π2 vvr

(2.53)

or,

The above Eqs. (2.52) and (2.53) are the equations for distribution curves of equal radial velocities of trailing vortexes in the RHI scanning mode for the disturbance field of trailing vortexes, in which the value range of θ is / | | | 2 (r, θ ) | 16π2 rc2 vvr | | kπ + α O + |arcsin |, | | Γ02 / |] | | 2 (r, θ ) | 16π2 rc2 vvr | | (k + 1)π + α O − |arcsin | 2 | | Γ0

[

(2.54)

where k = 0, 1, 2, . . . , n, in which n is a positive integer. [ / ] In addition, the radial velocity is the maximum at a point rc , α O − π 2 − 2kπ on the section of trailing vortexes. vvr max (rc , α O − π/2 − 2kπ) = vvr max (rc ) =

Γ0 4πrc

(2.55)

[ / ] Meanwhile, the radial velocity is the minimum at a point rc , α O − 3π 2 − 2kπ on the section of trailing vortexes. vvr max (rc , α O − 3π/2 − 2kπ) = −vvr max (rc ) = −

Γ0 4πrc

(2.56)

Therefore, the distribution curves of equal radial velocities of trailing vortexes in the polar coordinate system can be plotted based on Eqs. (2.52) and (2.53) for

2.4 Detection of Trailing Vortexes Based on the Coherent Doppler Lidar Fig. 2.22 Distribution curves of equal radial velocities of trailing vortexes of airbus A340

90

57

15 60

120 10 150

30 5m/s 10m/s 15m/s 20m/s

180

5

0

210

330

240

300 270

distribution curves of equal radial velocities of aircraft trailing vortexes in the RHI laser scanning mode and referring to simulation parameters of the civil airbus A340 under typical conditions provided in Table 2.3 in Sect. 2.3.4. The figure shows curves of equal radial velocities of trailing vortexes of A340 in the polar coordinate system. Comparison reveals that the lower the radial velocity is, the larger the area of circles enclosed by these curves; and vice versa. For example, the area of the circle enclosed by the radial velocity curve of vvr = 5m/ s is much larger than that enclosed by the radial velocity curve of vvr = 20m/ s (Fig. 2.22). Moreover, the two-dimensional (2D) sections of radial velocity distribution of trailing vortexes of airbus A340 under typical flight conditions are simulated and drawn, as displayed in Fig. 2.23. This is based on Eqs. (2.52) and (2.53) for distribution curves of equal radial velocities of trailing vortexes, by referring to the HallockBurnham model for the tangential velocity of trailing vortexes in Sect. 2.3.2, and considering interaction between left and right trailing vortexes (vector superposition described by Eq. (2.44) in Sect. 2.3.2). By analyzing the distribution section for radial velocity of trailing vortexes of airbus A340 in Fig. 2.23, the following conclusions about the distribution characteristics of the radial velocity of trailing vortexes can be obtained: (1) Because of the interaction between left and right trailing vortexes, the velocity components of each trailing vortex change while the overall radial velocity distribution of trailing vortexes is highly symmetric. (2) The aircraft trailing vortexes have positive and negative radial velocities, which are distributed completely symmetric. The closer an area to the vortex core centers is, the denser the distribution curves of equal radial velocities in the area

58

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

Fig. 2.23 Section for radial velocity distribution of trailing vortexes of airbus A340

and the larger the radial velocity; the farther an area to the vortex core centers is, the sparser the distribution curves of equal radial velocities in the area and the lower the radial velocity. (3) The radial velocity vvr of aircraft trailing vortexes is in direct proportion to the vortex circulation Γ0 . The larger the aircraft weight m or the lower the flight velocity vp is, the larger the vortex circulation Γ0 of the generated trailing vortexes, the higher the radial velocity vvr , and the wider the range with the same velocity. 2.4.2.2

Doppler Model for Echoes from Trailing Vortexes

The Doppler model for laser echoes from trailing vortexes is established by combining with distribution characteristics of the radial velocity in the disturbance field of trailing vortexes in RHI laser scanning mode and referring to distribution curves of equal radial velocities of trailing vortexes of airbus A340 drawn in simulation. Doppler spectra are results of joint action of motion and geometric structures of targets and the above curves of equal radial velocities of trailing vortexes can be completely regarded as description of the lidar for distribution characteristics of radial velocities of trailing vortexes. Considering this, Doppler spectra for echoes from trailing vortexes can be built according to the following idea: The area of the circle enclosed by radial velocity curve vvr is calculated, that is, the area A of the circle in which the radial velocity is v > vvr , which can be expressed as follows: {π/ 2 A=

r{(vvr )

dθ −π/ 2

r dr 0

(2.57)

2.4 Detection of Trailing Vortexes Based on the Coherent Doppler Lidar

59

The following two equations are obtained by slightly transforming Eqs. (2.52) and (2.53) for distribution curves of equal radial velocities of trailing vortexes: ] 21 [ Γ02 Γ0 sin(αO − θ ) 1 2 2 r (vvr ) = + sin (α O − θ ) − 4rc 2 4πvvr 2 4π2 vvr

(2.58)

] 21 [ Γ02 Γ0 sin(αO − θ ) 1 2 2 − sin (α O − θ ) − 4rc r (vvr ) = 2 4πvvr 2 4π2 vvr

(2.59)

and

To study influences of the aircraft type parameters, flight parameters, and environmental parameters on Doppler spectra for echoes from trailing vortexes, Eqs. (2.58) and (2.59) can be simplified through approximate processing [23]. According to the analysis, r (vvr ) in the equations is closely related to the vortex circulation Γ0 and the vortex core radius rc . The comparison between Γ0 = mg/ρvp s1lws and rc = 0.052s1 lws also reveals that the vortex core radius rc is smaller than the wingspan lws , while the vortex circulation Γ0 is greatly affected by the wingspan lws . Hence, it is approximated that rc = 0 in above equations. By doing so, Eqs. (2.58) and (2.59) can be simplified as r (vvr ) =

Γ0 sin(α O − θ ) 2πvvr

(2.60)

Therefore, the area A of the circle in which the radial velocity is v > vvr can be expressed as follows: {π/2 A=

r{(vvr )

r dr =

dθ −π/2

= −π/2

r2 2

] 2πvΓ0vr sin(α O −θ) 0

sin(α O −θ)

{

dθ

r dr

−π/2

0

−π/2 { [

Γ0 2πvvr

{π/2

0

[

Γ0 dθ = 2πvvr

]2 {π/2 sin2 (α O − θ )dθ

(2.61)

0

where α O is a known constant. Through further integral operation, the area of the circle can be obtained. [

Γ0 A= 2πvvr

]2 {π/2 {π/2 Γ02 Γ02 2 sin (α O − θ )d(α O − θ ) = sin2 (ψ)d(ψ) = 2 2 4πvvr 16π vvr 0

0

(2.62)

60

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

Besides, according to the schematic diagram for curves of equal radial velocities of trailing vortexes, the Doppler spectrum is expressed as follows: {π/ 2

r (vvr{+Δvvr )

S(vvr ) =

vvr dθ r dr −π/ 2

(2.63)

r (vvr )

Supposing that the resolution of radial velocity is high enough (the interval of two adjacent radial velocities is Δvvr → 0), the Doppler spectrum for echoes from trailing vortexes can be regarded as the differential calculus for the area A of the circle enclosed by the radial velocity v > vvr ( ) Γ02 Γ02 d dA = = S(vvr ) = 2 3 dυvr dvvr 16πvvr 8πvvr

(2.64)

According to analysis of Eq. (2.64), the amplitude of Doppler spectra for echoes from trailing vortexes is inversely proportional to the cube of the radial velocity while directly proportional to the square of the vortex circulation Γ0 . For aircraft trailing vortexes with a large vortex circulation, an obvious Doppler spectral width is shown for laser echoes, which means that the / larger Γ0 is, the greater the velocity variance. Based on the relation Δ f d = 2vvr λ0 between the radial velocity vvr and Doppler shift Δ f d , the Doppler frequency spectrum for echoes from trailing vortexes can be obtained. S(Δ f d ) =

Γ02 πλ30 Δ f d3

(2.65)

Moreover, to more intuitively reflect dependence of Doppler spectra for echoes from trailing vortexes on the type parameters, flight parameters, and / environmental parameters, the vortex circulation Γ0 = mg/ρvp s1lws and s1 = π 4 are substituted in Eq. (2.64). In this way, the Doppler spectrum for echoes from trailing vortexes can be expressed in another form [ S(vvr ) =

mg ρs1 vplws

]2

]2 [ ]2 [ 1 2 g m 1 = · · · 3 3 3 8πvvr π ρ vp · lws vvr

(2.66)

where the environmental parameters ρ and g separately refer to air density and gravitational acceleration; aircraft type parameters m and lws separately represent the aircraft weight and wingspan; flight parameter vp refers to the flight velocity. It can be seen from Eq. (2.66) that the Doppler spectrum for echoes from aircraft trailing vortexes is closely related to the aircraft type parameters, flight parameters, and environmental parameters.

2.4 Detection of Trailing Vortexes Based on the Coherent Doppler Lidar

61

In summary, Eqs. (2.64) ~ (2.66) are the mathematical models for Doppler spectra for echoes from aircraft trailing vortexes in the RHI laser scanning mode established based on theoretical deduction.

2.4.2.3

Simulation Analysis

Based on mathematical models Eqs. (2.64) ~ (2.66) for Doppler spectra for echoes from aircraft trailing vortexes deduced according to the above theory and taking airbus A340 under typical flight conditions as an example, Doppler spectra for echoes from trailing vortexes are plotted (Fig. 2.24) referring to the aircraft type parameters, flight parameters, and environmental parameters in Table 2.3 in Sect. 2.3.4. By analyzing Fig. 2.24, Doppler spectra for echoes from aircraft trailing vortexes have the following characteristics compared with atmospheric turbulent flows or high-altitude wind shear: (1) Symmetry Due to the high symmetry of tangential velocity distribution of trailing vortexes, their Doppler spectra for echoes are also symmetric (areas of positive and negative Doppler velocities are symmetric about the area of zero velocity). In comparison, the Doppler spectral distribution for laser echoes from natural wind-field disturbances such as turbulent flows or wind shear is asymmetric due to the irregular velocity distribution. (2) Broadening When considering the disturbance field of trailing vortexes at a certain scale, its scattering for incident laser is mainly shown as the superposition effect of atmospheric aerosol particles and atmospheric molecules at various points on the section Fig. 2.24 Doppler spectrum for echoes from trailing vortexes of airbus A340

62

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

of trailing vortexes. Therefore, the Doppler spectra for echoes show spectral broadening and contain many broadened time-varying spectra. Such broadened spectral structure of many spectra is conducive to detection of echoes from trailing vortexes from the fixed clutter background. Additionally, the model analysis also shows that the larger the vortex circulation Γ0 of aircraft trailing vortexes is, the more obvious the Doppler spectral width of laser echoes. (3) Amplitude characteristics The amplitude S(vvr ) of Doppler spectra for echoes from trailing vortexes is inversely proportional to the cube of radial velocity vvr (Doppler shift Δ f d ) while directly proportional to the square of vortex circulation Γ0 . In addition, the amplitude is also influenced by the aircraft type parameters (aircraft weight m and wingspan lws ), flight parameter (flight velocity vp ), and environmental parameters (air density ρ and gravitational acceleration g). The numerical characteristics of the amplitude are exclusive to Doppler spectra for echoes from trailing vortexes and induced by the tangential velocity distribution of trailing vortexes. They are also the most important characteristics for distinguishing trailing vortexes from turbulent flows or wind shear. Figure 2.25 illustrates Doppler spectra for echoes from trailing vortexes of airbuses A320, A340, and A380 measured by Thales Company using a narrow-beam highrange-resolution pulsed Doppler radar at Orly Airport in 2006 [24]. Because a pulsed Doppler lidar also has characteristics including a narrow beam and high range resolution, its description for distribution characteristics of the radial velocity of aircraft trailing vortexes is basically same as the narrow-beam high-range-resolution pulsed Doppler radar. Comparison shows that Doppler spectra for echoes from trailing vortexes of A340 obtained using the mathematical models of Doppler spectra established here are approximated to Doppler spectra for echoes (envelops) measured by Thales using the radar. The two are both symmetric and broadened. Particularly, the amplitude of

Fig. 2.25 Doppler spectra for echoes from trailing vortexes measured by Thales using a radar

2.5 Identification Trailing Vortexes of Airplanes and Parameter Extraction …

63

Doppler spectra is also inversely proportional to the cube of radial velocity. Considering the consistency between model simulation and field experimental measurement, it is proved that the above Doppler spectral models for echoes from trailing vortexes built through theoretical deduction are correct. This also provides reliable theoretical support for subsequent design of the identification algorithm for wind-field disturbances based on Doppler spectral features.

2.5 Identification Trailing Vortexes of Airplanes and Parameter Extraction Based on Laser Echoes Laser echoes do not directly contain characteristic parameters of trailing vortexes but contain Doppler shift information caused by atmospheric wind fields. Therefore, it is necessary to design corresponding parameter extraction and processing algorithms for trailing vortexes, thus realizing the real-time and effective acquisition of characteristic parameters of trailing vortexes during flight control. At first, starting from processing of lidar echo signals, a parameter extraction algorithm for trailing vortexes based on lidar echoes is designed through solution of velocity distribution and inversion of trailing vortex parameters. Meanwhile, a parameter processing method for trailing vortexes that integrates lidar detection and model prediction is proposed based on the particle filter theory and it is used to correct erroneous data caused by unstable disturbances. Finally, the proposed extraction algorithm and processing method are verified through simulation results.

2.5.1 Preprocessing of Echoes of Lidar Detection Laser echo signals not only contain backscattered signals of aerosols that are needed but also include noises including background light. Moreover, noises are also generated in the laser transmission and detection process [25]. To eliminate influences of these noises and then improve the SNR, the echo signals need to be preprocessed at first. Echo signals of a lidar feature a low SNR, so the signal averaging technique is generally applied to improve the SNR.√Through m times of accumulation and averaging, the SNR can be improved by m times. For a lidar with the repetition frequency of 50 Hz, it takes 2 s to realize 100 times of accumulation and averaging. Obviously, a large amount of accumulation and averaging cannot meet the requirement for real-time laser detection and processing of aircraft trailing vortexes. Wavelet method finds it difficult to determine the wavelet threshold and type when analyzing echo signals of lidars in different ranges and lacks adaptivity. Empirical mode decomposition (EMD), as a novel processing method for analyzing nonlinear, non-stationary signals, can effectively extract the trend of a data series and remove

64

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

high-frequency noises in the series [26]. EMD can real-timely process echo signals and improve the SNR and is adaptive. Therefore, the laser echo signals are subject to five times of pulse accumulation at first and then real-timely processed using the EMD method [27]. In EMD, the functions that meet the following conditions are defined as intrinsic mode functions (IMFs): ➀ the sum of the numbers of maximum and minimum points has a difference not larger than 1 with the number of zero-crossing points; ➁ the mean value of envelops separately constituted by maximum and minimum points needs to approximate to 0 everywhere. The essence of the EMD method is to decompose a signal into the sum of several IMFs and different IMFs have different scale characteristics, which enable detailed analysis. The process of pulse accumulation is not expounded here. The process of EMD processing is shown as follows: supposing that the original signal is f (t), then its decomposition (or selection) process includes: ➀ finding the local maximum and minimum of signal f (t), which are connected to form the upper envelop v(t) and lower envelop u(t) using the cubic spline function, and their mean value is solved to be m 1 (t) = 21 [v(t) + u(t)]; ➁ examining p1 (t) = f (t) − m 1 (t). If the obtained new signal p1 (t) does not meet the basic requirements for IMFs, the above operations are repeated for p1 (t) to attain p11 (t) = p1 (t) − m 11 (t); if p11 (t) still does not meet the basic conditions for IMFs, the above operations are repeated for p11 (t) to obtain p12 (t) = p11 (t) − m 12 (t). The process is repeated until p1k (t) = p1(k−1) (t) − m 1k (t) of an integer k satisfies the basic conditions for IMFs; ➂ defining c1 (t) = p1k (t), that is, the first IMF is separated from the original signal, which includes the part of the smallest local scale; ➃ recording f (t) − c1 (t) = r1 (t) and step ➀ is repeated for r1 (t) to obtain c2 (t), thus attaining the second IMF. Afterwards, letting r2 (t) = r1 (t)−c2 (t), the above operations are repeated and not stopped until rn (t) is basically in a monotonic trend or |rn (t)| is so small that can be regarded as the measurement error. In this way, rn (t) = rn−1 (t) − cn (t) is obtained, so there is f (t) =

n Σ

c j (t) + rn (t)

(2.67)

j=1

By doing so, the original signal f (t) is decomposed into a linear combination of a set of IMFs, and the process is termed EMD. Noises (high-frequency components) in the signal are mainly concentrated in the first several IMFs. By subtracting these IMFs from the original signal, the noises can be removed. The flowchart of the EMD denoising algorithm is illustrated in Fig. 2.26. Through the above processing of echo signals, the SNR of laser echo signals is improved. Because the radial velocity of atmospheric molecules or aerosol particles at each point in trailing vortexes along laser beams needs to be calculated based on Doppler spectra for laser echoes from trailing vortexes, Doppler shift information of atmospheric echo signals at corresponding points is extracted. If the pulse emission time is t0 = m 0 Ts (Ts represents the pulse sampling interval and m0 denotes the initial sampling point), the range from the scattering

2.5 Identification Trailing Vortexes of Airplanes and Parameter Extraction …

65

Fig. 2.26 Flowchart of the EMD denoising algorithm

point of trailing vortexes represented by the mth sampling point to the radar is / R = c(m − m 0 )Ts 2. Given the angular information θ provided by the scanning instrument, echo signal JB (R, α) at a certain point in the scanning area is obtained. To acquire the Doppler spectra for echoes at different ranges Rl = c(m l −m 0 )Ts /2, M sampling results JW (m k Ts , Rl , θi ) (k = 0, 1, 2, . . . , M − 1) of the backscattered signal JB (Rl , θi ) can be selected. In addition, fast Fourier transform (FFT) is performed for the sampled signal JW to acquire the Doppler spectrum S( f + kΔ f, Rl , θi ) for laser echoes at the scattering point (Δ f = (M Ts )−1 represents the frequency resolution of the Doppler spectrum). To realize intensive measurement of aircraft trailing vortexes, a small fixed range interval ΔR is selected to carry out Doppler transform (taking ΔR = 3m for example). Although the range interval incurs serious overlap for range resolution units with the pulse width of hundreds of nanoseconds, such processing method is more favorable for accurately finding the locations of aircraft trailing vortex cores.

66

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

At the same time, the velocity resolution Δv = Δ f · λ/2 = λ/(2M Ts ) is jointly determined by the sampling time interval, sampling number, and laser wavelength. To increase the velocity resolution at a fixed sampling frequency, 3Mnull points are interpolated in M sampling points of JW using the interpolation method in Fourier transform, so that the point number of Fourier transform changes to four times of the original one. In this way, the velocity resolution is correspondingly improved by four times.

2.5.2 Identification of Trailing Vortexes of Airplanes Based on Doppler Spectral Characteristics A Doppler spectrum describes the relative strength of the Doppler frequency component (radial velocity) of targets in a certain time duration, which is the result of combined action of motion and geometric structure of targets. Therefore, the types of wind-field disturbances (wind-field disturbances caused by aircraft trailing vortexes or natural wind-field disturbances) are expected to be distinguished by analyzing the extracted features of Doppler spectra for echoes from wind-field disturbances. The following analyses are all carried out on the condition that the measurement capacity of Doppler frequency spectra is met [28].

2.5.2.1

Selection of Spectral Features

Feature selection needs to take many factors into account and these basic principles need to be followed: ➀ features need to contain as much target information as possible; ➁ features need to be independent or uncorrelated; ➂ the dimension of features should be as low and simple as possible. Combining with the Doppler spectral features of trailing vortexes obtained in the above modeling and analysis, here the symmetry, waveform entropy, and numerical features of amplitude of Doppler spectra are mainly extracted. (1) Symmetry feature Doppler spectra for echoes from aircraft trailing vortexes are an even function taking the point of zero-frequency shift (where the radial velocity is zero) as the axis of symmetry; while Doppler spectra of natural wind-field disturbances such as turbulent flows and wind shear are asymmetric due to irregularity of these disturbances. Therefore, the symmetry of Doppler spectral distribution can be taken as an important feature for differentiating the types of wind-field disturbances. Besides, the residual of even function S is used to describe the symmetry of Doppler spectral distribution. The higher the symmetry of the spectral distribution is, the lower the value of the residual of even function S; otherwise, the value of S is larger. S can be expressed as follows:

2.5 Identification Trailing Vortexes of Airplanes and Parameter Extraction … N / 2−1 N Σ | f i − f N −1−i | S= 2 i=0

67

(2.68)

where f i (i = 0, 1, . . . , N − 1) represents the range-of-points waveform of a Doppler spectrum. Accordingly, the following judgment conditions for the type of wind-field disturbances can be set combining with the symmetry feature of Doppler spectral distribution: { Wtarget if S < Sth W ∈ (2.69) Wnature if S ≥ Sth where W denotes the type of wind-field disturbances; Wtarget and Wnature separately represent the target and natural wind-field disturbances; Rth is the constant judgment threshold, the value of which is pre-set. (2) Waveform entropy feature Existing research has found that Doppler spectra for echoes from aircraft trailing vortexes contain many broadened time-varying spectra and show the broadening characteristic. In comparison, Doppler spectra of natural wind-field disturbances such as turbulent flows and wind shear do not have such broadened spectral structure of many spectra. Therefore, the spectral broadening feature is expected to distinguish the types of wind-field disturbances. Entropy is generally used to measure the concentration degree in posterior probability distribution in the information theory. Considering this, if Doppler spectra for echoes from wind-field disturbances are regarded as a probability density function, the value of entropy reflects the broadening degree of Doppler spectra. The larger the value of entropy is, the wider the Doppler spectra; and vice versa. Therein, the waveform entropy of Doppler spectra for echoes from wind-field disturbances can be defined as follows: E =−

N −1 Σ

pi ln pi

(2.70)

i=0

where pi represents a Doppler signal expressed as the probability density function, as shown below: || N −1 || || Σ || || || | f i ||| (2.71) pi = | f i |/|| || || i=0

On this basis, the following judgment condition for the types of wind-field disturbances can be set by combining with the waveform entropy feature of Doppler spectra:

68

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

{ W ∈

Wtarget E ≥ E th Wnature E < E th

(2.72)

where W denotes the type of wind-field disturbances; Wtarget and Wnature separately represent the target and natural wind-field disturbances; E th is the constant judgment threshold, the value of which is generally pre-set and in practical application, it is determined by combining modeling and simulation as well as field experiments. (3) Numerical feature of amplitude Previous modeling has shown that the amplitude of Doppler spectra for echoes from trailing vortexes is in inverse proportion to the cube of the radial velocity (Doppler shift). The numerical feature of amplitude is exclusive to Doppler spectra for echoes from trailing vortexes, so it is the most critical feature for distinguishing trailing vortexes from natural wind-field disturbances including turbulent flows or wind shear. To express the numerical feature of amplitude of Doppler spectra (inversely proportional to the cube of radial velocity), the logarithmic residual Q is introduced, which is expressed as follows: Q=

N −1 Σ

ln f i + 3 ln Yi − C

(2.73)

i=N /2

where f i denotes a signal in positive-frequency domain of Doppler spectra; Yi represents the radial velocity corresponding to the frequency shift f i ; C is a constant. Yi and C can separately be calculated using the following equations: Yi = λ0 f i /2

(2.74)

( ) C = ln Γ02 /8π

(2.75)

The analysis shows that the more obvious the inverse proportion feature of the amplitude of Doppler spectra for echoes from wind-field disturbances to the cube of radial velocity, the logarithmic residual L is more approximated to 0; otherwise, if the amplitude of Doppler spectra is not in inverse proportion to the cube of radial velocity, the logarithmic residual L departs from 0. Therefore, the logarithmic residual L can well describe the numerical feature that the amplitude of Doppler spectra is in inverse proportion to the cube of radial velocity. On this basis, the following judgment condition for the types of wind-field disturbances can be set according to the numerical feature of amplitude of Doppler spectra: { W ∈

Wtarget Q < Q th Wnature Q ≥ Q th

(2.76)

2.5 Identification Trailing Vortexes of Airplanes and Parameter Extraction …

69

where W is the type of wind-field disturbances; Wtarget and Wnature separately represent the target and natural wind-field disturbances; Q th is the constant judgment threshold, the value of which can be directly set using the mathematical models of Doppler spectra.

2.5.2.2

Algorithm Design

The above section reveals that the symmetry feature, waveform entropy feature, and numerical feature of amplitude can be extracted from Doppler spectra for echoes from trailing vortexes. Whereas Doppler spectra for echoes from sudden natural wind-field disturbances including turbulent flows and high-altitude wind shear do not have these features. Therefore, a simple and fast identification algorithm can be designed using the above features to correctly and effectively distinguish types of wind-field disturbances acquired through laser detection and then used as the criterion for judging whether there is an aircraft target in the airspace. The identification algorithm for types of wind-field disturbances based on Doppler spectral features is proposed, and the flowchart of the algorithm is shown in Fig. 2.27. Figure 2.27 indicates that the identification algorithm for types of wind-field disturbances based on Doppler spectral features mainly includes the following three steps: preprocessing of echo data of wind-field disturbances, extraction of Doppler spectral features, and identification of types of wind-field disturbances.

Fig. 2.27 Flowchart of the identification algorithm for types of wind-field disturbances based on Doppler spectral features

70

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

(1) Preprocessing of echo data of wind-field disturbances: to remove noises in echo signals and extract more complete and real-time features, the original echo signals need to be preprocessed. The preprocessing is mainly to obtain the range-of-points waveform f = ( f 0 , f 1 , . . . , f N −1 ) of Doppler spectra through three processing links, namely, denoising, FFT, and normalization. (2) Extraction of Doppler spectral features: compared with sudden natural windfield disturbances such as turbulent flows and wind shear, the Doppler spectra of target wind-field disturbances caused by aircraft trailing vortexes mainly have symmetry feature, waveform entropy feature, and numerical feature of amplitude. Therefore, the symmetry feature (residual of even function S), waveform entropy feature (waveform entropy E), and numerical feature of amplitude (logarithmic residual Q) of Doppler spectra can be extracted based on the range-of-points waveform f = ( f 0 , f 1 , . . . , f N −1 ) of Doppler spectra attained through preprocessing of echo data. (3) Identification of types of wind-field disturbances: based on preprocessing of echo data of wind-field disturbances and extraction of Doppler spectral features, the types of Doppler spectra of wind-field disturbances can be judged from three features: symmetry feature, waveform entropy feature, and numerical feature of amplitude. If the three features meet the judgment conditions at the same time, the Doppler spectra belong to aircraft trailing vortexes, that is, the detected wind-field disturbances are target wind-field disturbances; otherwise, the windfield disturbances are natural wind-field disturbances such as turbulent flows and wind shear. The specific judgment conditions for features can refer to the section for selection of spectral features. 2.5.2.3

Simulation and Verification

The section verifies the feasibility of the identification algorithm for types of windfield disturbances based on Doppler spectral features using the modeling results of aircraft trailing vortexes. Taking target wind-field disturbances, that is, trailing vortexes of the airbus A340, as the identification object, whether the algorithm can identify aircraft trailing vortexes or not is judged by extracting and analyzing the Doppler spectral features, which mainly involves the following processes: (1) Data simulation of trailing vortexes: the distribution data of radial velocity are obtained by combining with the distribution data of wind-field disturbances of trailing vortexes of airbus A340 under typical flight conditions attained by simulation in Sect. 2.3.3. (2) Extraction of Doppler spectra: N points are randomly selected in the simulated radial velocity distribution field of trailing vortexes as the corresponding N backscattered echo points attained by lidar scanning of trailing vortexes, so as to characterize the whole disturbance field of trailing vortexes. According to the radial velocities at the above N backscattered echo points, the velocity distribution is solved and the Doppler spectra of trailing vortexes are extracted.

2.5 Identification Trailing Vortexes of Airplanes and Parameter Extraction …

71

(3) Calculation of feature identification rate: based on the Doppler spectra of trailing vortexes obtained above, the symmetry feature (residual of even function S), waveform entropy feature (waveform entropy E), and numerical feature of amplitude (logarithmic residual Q) of Doppler spectra are extracted and the corresponding thresholds are set for judgment. The Monte Carlo experimental method is adopted to separately calculate the identification rate based on the above three Doppler spectra features. The number of Monte Carlo experiments is 10,000. Under conditions of different numbers N of randomly sampled scattering points (N = 3000, 5000, 7000), Doppler spectra are separately extracted from simulation data of trailing vortexes of airbus A340, as displayed in Fig. 2.28. The judgment thresholds for the above three features are set as follows: the symmetry feature (residual of even function is Sth = 0.38), waveform entropy feature (waveform entropy is E th = 5.38), and numerical feature of amplitude (logarithmic

Fig. 2.28 Doppler spectra of trailing vortexes extracted under conditions of different numbers of sampling points

72

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

residual is Q th = 0.21). Then, the identification rates based on each Doppler spectral feature under conditions of different numbers N of randomly sampled scattering points are calculated, as listed in Table 2.6. By analyzing the calculation results of identification rates based on features in Table 2.6, the Doppler spectrum is accurately identified to belong to aircraft trailing vortexes based on the symmetry feature, waveform entropy feature, and numerical feature of amplitude of Doppler spectra. Figure 2.29 shows the relationships of identification rates based on the symmetry feature, waveform entropy feature, numerical feature of amplitude with the number of randomly sampled scattering points. By analyzing Fig. 2.29, it can be seen that as the number N of randomly sampled scattering points constantly grows, the identification rates based on each feature exhibit a constant increase trend. Under conditions of the same number N of randomly sampled scattering points, the identification rate based on the numerical feature of amplitude is lower than that based on the waveform entropy feature; while the identification rate based on the waveform entropy feature is lower than that based on the symmetry feature. Therefore, when using the above Doppler spectral features to differentiate the type of wind-field disturbances, identification based on the numerical feature of amplitude is most rigorous, followed by that based on the waveform entropy feature, and identification based on the symmetry feature is less rigorous. The analysis of the calculation example using the above algorithm shows that the identification algorithm for types of wind-field disturbances based on Doppler spectral features is characterized by advantages including stable features, small calculation amount, fast judgment speed, and good identification effect. It can simply and accurately distinguish types of wind-field disturbances (being target wind-field disturbances of aircraft trailing vortexes, or sudden natural wind-field disturbances such as wind shear or turbulent flows), thus realizing the identification of disturbance fields based on trailing vortexes. Table 2.6 Identification rates of trailing vortexes based on Doppler spectral features Judgment features

Identification rates N = 3000 (%)

N = 5000 (%)

N = 7000 (%)

Symmetry feature (residual of even function S)

97

99

99

Waveform entropy feature (waveform entropy E)

40

87

99

Numerical feature of amplitude (logarithmic residual Q)

37

89

98

2.5 Identification Trailing Vortexes of Airplanes and Parameter Extraction …

73

Fig. 2.29 Relationships of identification rates based on Doppler spectral features with the number of randomly sampled scattering points

2.5.3 Parameter Extraction of Trailing Vortexes of Airplanes Based on Laser Echoes Laser echoes do not directly contain characteristic parameters of trailing vortexes but include Doppler shift caused by atmospheric wind fields. Therefore, it is necessary to design corresponding parameter extraction and processing algorithms for trailing vortexes, thus realizing the real-time and effective acquisition of characteristic parameters of trailing vortexes during flight control [29].

2.5.3.1

Calculation of Radial Velocity Distribution

After each time of scanning, echo signals in all range units are sampled, interpolated, and then subject to Doppler transform, thus obtaining a 3D Doppler spectrum S( f 0 + kΔ f, R0 +lΔR, θ0 + nΔθ ). Because the frequency shift information is equivalent to velocity information, the Doppler spectrum can be transformed into the 3D velocity spectrum W (v0 + kΔv, R0 + lΔR, θ0 + nΔθ ) through Eq. (2.77). v0 = λ f 0 /2, Δv = λΔ f /2

(2.77)

Taking the velocity spectrum W (V0 + kΔv, Ri , θ j ) for a spatial unit at the spatial coordinates (Ri , θ j ) relative to the radar as an example, a threshold Wthr is selected in the velocity spectrum and then the nearest two intersection points of the threshold with the spectrum in two sides of the peak are found. In this way, the positive and negative values of wind velocities at the spatial point are obtained, as indicated by v+ and v− in Fig. 2.30.

74

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

Fig. 2.30 Selection of the positive and negative velocities on the envelop of wind velocity

The v+ and v− of all range units constitute the positive and negative velocity envelops that represent velocity magnitude in the wind fields. Because two points closest to the peak are selected, some influences of noises are effectively eliminated. An appropriate threshold is selected. Then, the positive envelop obtained represents the maximum radial velocity of various detection units in the detection area, while the negative envelop represents the minimum value. In areas close to the vortex cores, the radial velocity is the projection of superposition of the tangential velocity induced by trailing vortexes and surrounding background wind-field velocity in the direction of light beams. The detailed calculation process is illustrated in Fig. 2.31. Based on the angular information provided by the scanning equipment and the range information obtained according to the echo time, the spatial distribution of radial velocity represented by Q(vvr , R, θ ) can be obtained. Here, (R,θ ) denotes the location of a spatial point in the polar coordinate system with the lidar as the origin, and vvr represents the average velocity vvr = (v+ +v− )/2 obtained according to positive and negative radial velocity envelops. In a wind-field area with the induction effect of trailing vortexes, the radial velocity is the superposition of the background wind field and the tangential velocity induced by trailing vortexes. To eliminate influences of background winds, one velocity envelop before and one after the trailing vortex can be selected and averaged, and the obtained velocity envelop is used as the background wind velocity in the detection. After subtracting the velocity from the above spatial distribution of radial velocity, the spatial distribution of radial velocity more approximated to that induced by practical trailing vortexes is obtained. Figure 2.32 shows the wind-field distribution data obtained through software simulation. In the set of data, the trailing vortex is near to (187, 1028), before and after which velocity envelops vA and vB are selected. Then, the background wind velocity at the trailing vortex is expressed as (vA +vB )/2.

2.5 Identification Trailing Vortexes of Airplanes and Parameter Extraction …

75

Fig. 2.31 Extraction process of positive and negative velocities in the velocity spectrum

Fig. 2.32 Determination of the background wind velocity in the presence of a trailing vortex

10

1200 1150

Rang/m

1100

5 VA 0

1050 1000

-5

950 900

VB

-10

850 800 185

2.5.3.2

-15 190

195

200 205 210 Elevation angle/deg

215

220

Parameter Inversion of Aircraft Trailing Vortexes

After the above processing of lidar echoes, the positive and negative velocity envelops have been solved, thereby obtaining the spatial distribution of radial velocity after eliminating the background atmospheric wind field. By further processing the velocity distribution information, disturbance quantities of trailing vortexes including the vortex core location, vortex core radius, and vortex circulation are extracted.

76

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

Figure 2.32 illustrates the coordinates of trailing vortexes detected using the lidar in the plane position indication (PPI) mode. The lidar is located at point O. (1) Vortex core location max max min min Taking the right vortex as an example, coordinates (RC1 , θC1 ) and (RC1 , θC1 ) separately of the maximum and minimum velocities are searched from the positive envelop v+ (R, θ ) and negative envelop. The coordinates of the midpoint on the line connecting the two coordinate points are taken as the estimated coordinates (RC1 , θC1 ) of the right vortex core, as shown in Eq. (2.78).

RC1 =

min max min + RC1 RC1 θ max + θC1 , θC1 = C1 2 2

(2.78)

After transforming the coordinates into the Cartesian coordinate system, there is xC1 = RC1 cos θC1 , z C1 = RC1 sin θC1

(2.79)

Similarly, the estimated polar coordinates (RC2 , θC2 ) and estimated rectangular coordinates (xC2 , z C2 ) of the left vortex core can be found. The coordinates at the center of the aircraft trailing vortex field are represented by those of the midpoint on the line connecting the two vortex cores, that is, xo =

z C1 + z C2 xC1 + xC2 , zo = 2 2

(2.80)

(2) Vortex core radius After finding the locations of vortex cores, the vortex core radius of the right vortex can be deduced according to locations of the maximum and minimum velocities found on the positive and negative envelops. (

rC1

max θ min − θC1 = RC1 sin C1 2

) (2.81)

(3) Vortex circulation The vortex circulation of trailing vortexes is closely related to the velocity distribution in the vortex field. At present, many foreign researchers have used the maximum likelihood estimation method to estimate the parameter. Friedrich et al. deduced the expression of vortex circulation in the RHI mode. By referring to the method max max and θ2 = θC2 are selected in the PPI in previous research, fixed angles θ1 = θC1 mode. Taking the range as a variable, ranges Ri and Rk (i = i 0 , i 0 +1, . . . , i 0 +n −1, j = j0 , j0 + 1, . . . , j0 + m − 1) are added up and averaged. Based on the relationship between the detected radial velocity by using the radar and the vortex circulation, the expression of vortex circulation in the PPI mode can be deduced. It is supposed that the vortex circulations of the right and left aircraft trailing vortexes are Γ1 and Γ2 .

2.5 Identification Trailing Vortexes of Airplanes and Parameter Extraction …

77

Fig. 2.33 Coordinates of trailing vortexes detected using the lidar in the PPI mode

The wind velocity in the disturbance field at point (Ri , θ1 ) in Fig. 2.33 is expressed as a vector (u 1 , v1 ) in the velocity coordinate system. Considering that the velocity field of each point in the space is the result of joint action of two trailing vortexes, there is { Γ2 Γ1 u 1 = 2πr 2 (H − z 1 ) + 2 (H − z 1 ) 2πr12 11 ( ) ( ) (2.82) Γ1 b0 Γ2 v1 = 2πr 2 x1 − 2 + 2πr 2 x1 + b20 11

12

where r11 and r12 separately represent the ranges from point (Ri , θ1 ) to two vortex cores; b0 is the distance between vortex cores; H is the height of trailing vortexes above ground. According to the transformational relation of coordinate systems, coordinates (x, z) in the Cartesian coordinate system can be transformed into the following polar coordinates: {

z 1 = Ri cos θ1 x1 = Ri sin θ1

(2.83)

The following is obtained by substituting Eq. (2.83) into Eq. (2.82): {

u1 = v1 =

Γ1 2 (H 2πr11 ( Γ1 Ri 2 2πr11

Γ2 − Ri sin θ1 ) + 2πr 2 (H − Ri sin θ1 ) 12 ( ) ) b0 Γ2 cos θ1 − 2 + 2πr 2 Ri cos θ1 + b20

(2.84)

12

The Doppler information contained in lidar echoes is the radial velocity of the trailing vortex field. In the radar scanning process of the whole trailing vortex field, max ) and the left and right vortexes are scanned. Therefore, detection points (Ri , θC1 max (Rk , θC2 ) in the vicinity of the two vortexes are selected, as displayed in Fig. 2.33 max max (θ1 = θC1 and θ2 =θC2 ). If the direction from the disturbance field to the lidar is set as the positive direction, then the radial velocities of the two disturbance points in the direction of light beams are expressed as

78

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

{

vvr1 (Ri , θ1 ) = −(cos θ1 u 1 + sin θ1 v1 ) vvr2 (Rk , θ2 ) = −(cos θ2 u 2 + sin θ2 v2 )

(2.85)

According to the geometrical relationships RC1 cos θC1 = b20 and RC1 sin θC1 = H , Eq. (2.84) is substituted into Eq. (2.85). After simplification, the following is obtained: { RC1 RC1 vvr1 (Ri , θ1 ) = Γ1 2πr 2 sin(θ1 − θC1 ) − Γ2 2 sin(θ2 − θC1 ) 2πr12 11 (2.86) RC2 RC2 vvr2 (Rk , θ2 ) = Γ1 2πr 2 sin(θ2 − θC2 ) − Γ2 2πr 2 sin(θ2 − θC2 ) 21

22

Under conditions of the same azimuth while different ranges, the radial velocity in range units within the disturbance range of trailing vortexes is substituted into the above equation set for calculation and averaging. Finally, the vortex circulations of the two trailing vortexes can be attained. Γ1 =

n m 1 Σ Σ vvr1 (Ri , θ1 )N2 (Rk , θ2 ) − vvr2 (Rk , θ2 )M2 (Ri , θ1 ) nm k=1 i=1 M1 (Ri , θ1 )N2 (Rk , θ2 ) − N1 (Rk , θ2 )M2 (Ri , θ1 )

(2.87)

Γ2 =

m n 1 Σ Σ vvr1 (Ri , θ1 )N1 (Rk , θ2 ) − vvr2 (Rk , θ2 )M1 (Ri , θ1 ) nm k=1 i=1 M1 (Ri , θ1 )N2 (Rk , θ2 ) − N1 (Rk , θ2 )M2 (Ri , θ1 )

(2.88)

where

2.5.3.3

M1 (Ri , θ1 ) =

RC1 sin(θ1 − θC1 ) 2π[(Ri cos θC1 − b0 /2)2 + (Ri sin θC1 − H )2 ]

(2.89)

M2 (Ri , θ1 ) =

RC1 sin(θ1 + θC1 ) 2π[(Ri cos θC1 + b0 /2)2 + (Ri sin θC1 − H )2 ]

(2.90)

N1 (Rk , θ2 ) =

RC2 sin(θ2 − θC2 ) 2π[(Rk cos θC2 − b0 /2)2 + (Rk sin θC2 − H )2 ]

(2.91)

N2 (Rk , θ2 ) =

RC2 sin(θ2 + θC2 ) 2π[(Rk cos θC2 + b0 /2)2 + (Rk sin θC2 − H )2 ]

(2.92)

Field Experimental Verification

By using the above wind lidar based on the iodine molecular filter, the laser detection experiments for trailing vortexes of civil aircraft targets were conducted. The schematic diagram for field experiments on lidar detection of aircraft trailing vortexes in the PPI mode is shown in Fig. 2.34. The lidar was placed below the flight path of the aircraft and was not far from the airport, which enabled observation of the flight direction of the aircraft and convenience of adjusting the scanning range of the lidar [29].

2.5 Identification Trailing Vortexes of Airplanes and Parameter Extraction …

79

Fig. 2.34 Schematic diagram for field lidar detection experiments of aircraft trailing vortexes

(1) Experimental steps The specific steps of field detection experiments include. (1) The vehicle-mounted wind lidar was placed at the experimental site near the airport and the rotating scanning mirror was adjusted to horizontal. The coordinate direction of scanning was calibrated. The experimental site was taken as the center. The horizontal south direction was set as the zero azimuth, with the counter-clockwise direction as the positive; the horizontal direction was set as the zero pitching angle, with the counter-clockwise direction as the positive. (2) The lidar was powered and adjusted to emit the laser beam straightly upwards to measure the sensitivity (for inversion of lidar echo data). Then, the computer control system of the lidar and the time of the chronograph were accurately calibrated. (3) The departure time and aircraft type parameters were recorded according to the flight schedule. When the aircraft flew overhead according as observed visually, the chronograph was used to record the overhead time and the range measuring equipment was adopted to measure the overhead range of the aircraft. A camera was used to record the flight video of the aircraft to further calculate the elevation angle and velocity of flight.

80

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

(a) Scanning before the aircraft (b) Scanning during the aircraft (c) Scanning after the aircraft entering the area entering the area entering the area again

Fig. 2.35 Experimental pictures for laser detection of wind-field disturbances caused by an aerial target

(4) The PPI mode was used. The azimuth and pitching angle of the pre-set scanning area were 60° ~ 120° and 35°, respectively. The laser beam was adjusted to the initial position of the pre-set scanning area. (5) The lidar was turned on after the aircraft took off while did not enter the pre-set scanning area in the experiments to scan the atmospheric wind field of the preset scanning area. After the scanning, the lidar was immediately turned back to the initial scanning position to scan the wind field in the pre-set scanning area again. (6) After the scanning ended, the lidar echo data were read and processed to separately obtain lidar echo data in each time of scanning. Figure 2.35 displays the field experimental pictures. (2) Experimental data analysis The experiments were conducted in the afternoon of September 14 using the PPI mode. The detection range, scanning range, scanning rate, pitching angle, angular resolution, and radial range unit were 300 ~ 1500 m, 60° ~ 120°, 3°/s, 35°, 2°, and 5 m, respectively. B744 with the wingspan of 66.44 m and fuselage length of 70.7 m was detected. The lidar detection parameters are listed in Table 2.7.

Table 2.7 Lidar scanning parameters for detection of trailing vortexes

Parameters

Values

Scanning mode

PPI

Scanning rate v/°·s

3

Scanning period T/s

20

Pitching angle α/(°)

35

Azimuth [θmin , θmax ]/(°)

[60, 120]

Detection range [Rmin , Rmax ]/m

[300,1500]

2.5 Identification Trailing Vortexes of Airplanes and Parameter Extraction …

81

In the experiments, a set of relatively ideal data were selected for analysis, including results of three times of scanning. The first scanning was conducted at 16:30:43 PM when the aircraft began to appear in the FOV while did not enter the scanning area of the laser beam. At the moment, the scanning area did not have disturbances caused by aircraft trailing vortexes, so the obtained wind-field information can be fully regarded as the background atmospheric wind field. In the second scanning at 16:31:14 PM, the aircraft had entered the scanning area, when the wind field in the scanning area was disturbed by the aircraft trailing vortexes, so the obtained information included disturbance information of trailing vortexes. The third scanning was performed at 16:32:36 PM when the aircraft left the scanning area. Because the time interval was only 0.5 min, the disturbances caused by trailing vortexes had not disappeared in the atmosphere while the location of trailing vortexes changed. By using the algorithm proposed in the section, three groups of radial velocity distribution in the wind field are obtained after processing echo signals and calculating the radial velocity distribution, as shown in Fig. 2.36. Figure 2.36a shows the calculation results of the first scanning data. The scanning data are background atmospheric wind field and no obvious vortex fields are found, with gentle wind velocities in most areas. Figure 2.36b illustrates the calculation results of the second scanning data, from which obvious aircraft trailing vortexes are found at the location with the range of 650 m and azimuth of 90°. Figure 2.36c displays the calculation results of the third scanning data, which indicate that disturbances caused by trailing vortexes are still precent and drift to the location with the range of 570 m and azimuth of 96°. By using the algorithm proposed in the section, parameters of the aircraft trailing vortexes are extracted. The results are compared with model simulation results in Sect. 2.3 and the comparison results are listed in Table 2.8. It can be seen from the comparison results that the extraction results of the algorithm are basically consistent with the simulation results. The calculation error of the vortex core radius is about 0.88 m and the errors of vortex circulations are separately 10 and 16 m2 /s, indicative of the effectiveness of the algorithm.

82

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

Fig. 2.36 Radial velocity distribution in the wind field before and after the aircraft entered the scanning area

105

1600

120

90

0

0

75

0

0

0

60 800m

1400

1200

700m 600m

1000

500m

800

400m 600 -15

-10

-600

-400

-5 -200

300m 5

0 0

200

10

400

600

800

(a) Radial velocity distribution in the wind field before the aircraft entered the scanning area 105

1600

120

90

0

0

75

0

0

0

60 800m

1400

1200

700m 600m

1000

500m

800

400m 600 -10

-5

-600

0

-400

-200

0

5

300m 10

200

400

15 600

800

(b) Radial velocity distribution in the wind field after the aircraft entered the scanning area 105

1600

120

90

0

0

75

0

0

0

60 800m

1400

1200

700m 600m

1000

500m

800

400m 600 -10 -600

-5 -400

0 -200

0

5 200

300m 400

10

15 600

800

(c) Radial velocity distribution in the wind field after the aircraft left the scanning area

References

83

Table 2.8 Comparison of experimental and simulation results for extraction of trailing vortex parameters Extraction of trailing vortex parameters

Units

Vortex core locations

[RC1 /m, θ [RC2 /m, θ

o C1 /( )] o C2 /( )]

Simulation results

Experimental results

Aircraft position

[657, 87] [660, 93]

Vortex core radius

r c /m

5.12

6

Vortex circulations

G1 /m2 ·s−1 G2 /m2 ·s−1

528 528

544 538

References 1. Wu YH, Hu YH, Gu YL, et al. Research on a new algorithm for obtaining air moving target information. J Opt. 2010;30(s1):9–13. 2. Zhang SD, Gao WY, Wang XQ, et al. Research on the wind speed characteristics of wind farms. Mech Des Manuf. 2010;8:150–1. 3. Yu W. Modeling of variable wind fields and flight simulation of high aspect ratio unmanned aerial vehicles. Xi’an: Northwest University of Technology; 2004. 4. Hefei Meteorological Network. http://www.hfqx.com.cn/hfqh/hfqh.htm 5. Frehlich R, Sharman R. Maximum likelihood estimates of vortex parameters from simulated coherent Doppler lidar data. J Atmos Oceanic Tech. 2005;22(11):117–30. 6. Yu YM, Feng QH, Xie M, et al. Research on VVP 3D wind field inversion method. J Sichuan Univ. 2008;45(2):321–6. 7. Frank B, Tatom SR. Simulation of atmosphere turbulent gusts and gust gradients. J Aircraft. 1981;19(14):264–271. 8. Hu YH, Yang X, Shi L, et al. A method for detecting airborne targets based on atmospheric disturbance coherent laser detection. ZL202010853553.7[P]. 2020-08-24. 9. Jin Y. Carbon dioxide coherent lidar for measuring wind speed and atmospheric disturbances. Optoelectromech Inform. 1996;13(10):1–6. 10. Tao XH, Hu YH, Zhao NX, et al. Performance analysis of atmospheric CO2 coherent detection lidar system. J Quantum Electron. 2010;25(2):230–4. 11. Wu YH, Hu YH, Dai DC, et al. Based on 1.5 μ research on aircraft wake vortex detection technology using M-Doppler Lidar. Acta Photonica Sinica. 2011;40(6):811–7 12. Holzäpfel F, Hofbauer T, Darracq D, et al. Analysis of wake vortex decay mechanisms in the atmosphere. Aerosp Sci Technol. 2003;7(4):263–75. 13. Xu SL, Hu YH, Guo LR. Design and performance analysis of a coherent laser detection system for aircraft wake vortices. Prog Laser Optoelectron. 2014;51(8):100–5. 14. Hu Y H, Yang X, Shi L, et al. Variable resolution detection method, storage medium, and system for atmospheric disturbance of aerial targets. ZL202011004999.9[P]. 2020-09-23. 15. Meng ZH, Hong GL, Hu YH, et al. Research on key technologies of chirped amplitude modulation coherent detection lidar. J Opt. 2010;30(8):2446–51. 16. Sasano Y, Kobayashi T. Feasibility study on space Lidars for photon-counting avalanche photodiodes applied to a laser radar system. Appl Opt. 2005;44(6):5140–6. 17. Yang CH, Sun DS, Li HJ, et al. Research on the application of photon accumulation method in imaging lidar. Infrared Laser Eng. 2005;34(5):517–20. 18. Hu Y H, Dong X, Yang X, et al. A large dynamic laser coherent detection device and method for high-speed moving targets. ZL202210631958.5[P].2022-06-07. 19. Zhu DW. Velocity model analysis of aircraft wake vortex. J Civil Aviation Flight Acad China. 2005;16(6):17–20. 20. Wu YH, Hu YH, Xu SL, et al. Design of airport wake vortex monitoring system based on 1.5-μ m pulsed coherent Doppler lidar. Optoelectron Lett. 2011;7(4):298–303

84

2 Laser Detection on Disturbance of Wind-Field of Air Moving Targets

21. Hu YH, Wu YH. Analysis of aircraft wake vortex characteristics and research on laser detection technology. Infrared Laser Eng. 2011;40(6):1063–9. 22. He DF. The impact of aircraft wing tip tail vortex on the flight safety of later aircraft and safety measures. J Civil Aviation Flight Acad China. 2005;16(1):12–4. 23. Liu JK, Wang XS, Wang T, et al. Doppler characteristics analysis of aircraft wake in humid atmosphere. Signal Process. 2009;25(9):1443–7. 24. Barbaresco F, Meier U. Radar monitoring of a wake vortex: electromagnetic reflection of wake turbulence in clear air. C R Phys. 2010;11(9):54–67. 25. Hu Y H, Shi L, Yang X, et al. A single/multiple target detection method based on coherent laser detection of airborne wake vortices. ZL202010853554.1[P]. 2020-08-24. 26. Rahm S, Smalikho I. Aircraft wake vortex measurement with airborne coherent Doppler lidar. J Aircr. 2008;45(4):1148–55. 27. Tao XH, Hu YH, Lei WH, et al. Empirical mode decomposition for lidar atmospheric echo processing. Laser Technol. 2008;32(6):590–2. 28. Xu SL, Hu YH, Wu YH. Aircraft wake vortex recognition based on Doppler spectral features. Optoelectron Laser. 2011;22(12):1826–1830 29. Xu SL, Hu YH, Zhao NX. Aircraft wake vortex parameter extraction based on lidar echoes. Acta Photonica Sinica. 2013;42(1):54–8.

Chapter 3

Laser Detection on Atmospheric Components Disturbed by Aerial Moving Targets

The huge amount of exhaust discharged by aerial targets such as airplanes after fuel combustion quickly changes concentrations of surrounding atmospheric compositions and particularly, the contents of CO2 and water vapor rise significantly. Such disturbances of the atmospheric compositions belong to typical target-derived attributes, which provide convenience for the detection of aerial moving targets. Lidars, which have high angular and range resolutions, not only can be used to detect atmospheric wind fields but also are applicable to detection of concentrations of atmospheric CO2 and water vapor. The chapter mainly expounds the detection principles, realization methods, and performance analysis of derived attributes of targets including disturbances of CO2 and water–vapor concentrations induced by aerial moving targets.

3.1 Laser Detection on Atmospheric CO2 Disturbance The exhaust discharged by aerial moving targets such as airplanes after combustion of hydrocarbons mainly comprises H2 O and CO2 . The CO2 concentration in the background atmosphere is relatively stable, so it is CO2 gas in the exhaust that greatly disturbs the surrounding atmosphere. Considering this, the presence of a target or not can be sensed by detecting changes in the CO2 concentration in different ranges and directions. Because of the high detection signal-to-noise ratio (SNR) of differentialabsorption systems, a range-resolution differential-absorption lidar (DIAL) system is used to detect the CO2 concentration. The section mainly introduces the basic principles, detection schemes, and key technologies for detecting disturbances of the atmospheric CO2 concentration.

© National Defense Industry Press 2023 X. Yang and Y. Hu, Photoelectric Detection on Derived Attributes of Targets, https://doi.org/10.1007/978-981-99-4157-5_3

85

86

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

3.1.1 Characteristics of Atmospheric CO2 Disturbance by Targets As early as the end of the nineteenth century, the human has ascertained that the earth’s atmosphere is a mixture of multiple gases and contains water vapor and some impurities. In the lower atmosphere below the altitude of 80 km, gas compositions can be divided into two parts: one is invariable compositions, mainly referring to N2 , O2 , and Ar, which remain fixed proportions that do not change with time and space; the other is variable compositions, mainly including water vapor, CO2 , and O3 , among which water vapor changes most greatly, while CO2 and O3 have the lowest proportions while heavy influences on the climate. Clean dry air refers to the whole gas mixture excluding water vapor, liquid, and solid particles in the atmosphere and it is commonly shorted as dry air. Clean dry air is mainly composed of N2 , O2 , Ar, and CO2 , the collective volume content of which accounts for more than 99.99% of all clean dry air. Apart from these, there are also little amounts of H2 , Ne, and O3 , as listed in Table 3.1. Due to air movement and molecular diffusion in the atmosphere, the air at different altitudes and in different regions can be exchanged and mixed. Proportions of compositions of clean dry air in the lower atmosphere basically do not change. According to division of the atmosphere, the troposphere is a layer close to the ground and most greatly affected by the ground. Because the air near the ground rises after being heated while the cold air above sinks to induce convective motion, this layer is called the troposphere. The lower bound of the troposphere is the ground while the upper bound varies with latitude and season. The upper bounds are at altitudes of 18 km, 10–12 km, and only 8–9 km separately in low-latitude, mid-latitude, and high-latitude regions, and the troposphere in summer is thicker than that in winter. Taking Hefei City (Anhui Province, China) as an example, the troposphere thickness in summer can be as large as 15 km, while it is smallest in winter, being only 11 km. The layer from the top of the troposphere to the altitude of 50–55 km is called the stratosphere because air movement in the layer is dominated by horizontal movement. Table 3.1 Compositions of clean dry atmosphere

Gases

Volume fractions

Mass fractions

Molecular weights

N2

78.084

75.52

28

O2

20.948

23.15

32

Ar

0.934

1.28

40

CO2

0.033

0.05

44

3.1 Laser Detection on Atmospheric CO2 Disturbance

3.1.1.1

87

Conventional Spatial Distribution Characteristics of Atmospheric CO2

Atmospheric CO2 mainly comes from natural factors and human activities. The former includes volcanic eruption, forest fire, and plant decay, while the latter mainly refers to combustion. In the whole troposphere, although the CO2 concentration shows significant seasonal and latitudinal variation, the annual mean CO2 concentration is basically stable. CO2 is fully mixed in the atmosphere. In 1980s, the intrinsic content of CO2 in the altitude range of 0–10 km was about 322 × 10–6 (excluding direct influences of human activities, and it could be as high as 500 × 10–6 in some industrial regions); it was 321 × 10–6 in the lower stratosphere of 11–20 km; while the CO2 content declines sharply at higher altitudes, and it is only 0.6 × 10–6 in the higher stratosphere. Because human activities become increasingly active, the CO2 concentration in the troposphere constantly increases slowly. Observation shows that the CO2 concentration has risen from 310 × 10–6 in 1950s to 400 × 10–6 currently, at an annual growth rate of 2 × 10–6 . Keeling curves released by the Mauna Loa Observatory of the US in April, 2013 are displayed in Fig. 3.1. The CO2 content in the atmosphere is significant for the climatological research on global mean temperature changes, so the whole world has paid close attention to changes in the CO2 concentration. Figure 3.2 illustrates changes in the CO2 concentration over a day measured using a lidar at 2 μm [1]. Figure 3.3 shows fixed-point measurements of the CO2 concentration every 10 min in Nanjing City (Jiangsu Province, China) on July 20, 2005 [2]. Figure 3.4 displays changes in the CO2 concentration in one week measured by the NASA on May 30, 2004. The CO2 concentration is shown to change gently at the one-hour time scale, while the changes are minimal at the scale of seconds. At present, only Hong Guanglie et al. at Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences in China have studied Raman scattering Fig. 3.1 Growth of the annual mean atmospheric CO2 concentration

88

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

Fig. 3.2 Changes in the atmospheric CO2 concentration within 8 h

Fig. 3.3 Fixed-point measurements of the average CO2 concentration every 10 min

Fig. 3.4 Changes in the CO2 concentration in one week measured by the NASA on May 30, 2004

3.1 Laser Detection on Atmospheric CO2 Disturbance 8-(21:42)

600

9-(21:59)

600

550

550

500

500

450

450

400

400

350

350

300

ppmv

ppmv

89

250

300 250

200

200

150

150

100

100

50

50

0

0

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Height(km)

1.4

1.6

1.8

2.0

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Height(km)

1.4

1.6

1.8

2.0

Fig. 3.5 The CO2 concentration in Hefei measured in the radar experiments on Raman scattering

radar systems for detecting the atmospheric CO2 concentration and detected the atmospheric CO2 concentration in Hefei. Figure 3.5 displays their experimental results of the atmospheric CO2 concentration detected from 21:42 to 21:59 P.M. on June 28, 2005. They concluded that the CO2 content in the atmosphere changes slightly with the altitude, and the near-ground CO2 content in Hefei fluctuates in 350–400 × 10–6 [3]. Only a few factors influence changes in the CO2 concentration high in the air, so the CO2 concentration is more stable than that near the ground. Therefore, the above groups of data reveal that disturbances of the conventional atmospheric CO2 concentration and changes so subtly at a small scale and within several seconds that they can be regarded as constant. This is conducive to the early warning and detection of targets. During atmospheric CO2 detection within a short time period and at a small scale, if the CO2 concentration changes substantially and rises abruptly, it may be disturbances caused by targets, thus providing early warning for targets.

3.1.1.2

Mechanism of Disturbances of the Atmospheric CO2 Concentration Induced by Targets

The power sources of engines of aerial flying targets (airplanes) are mainly aviation kerosene. Ordinary kerosene is formed by mixing multiple hydrocarbons with different chemical compositions and properties. Ordinary kerosene and aviation kerosene share the same molecular formula of CH3 (CH2 )k CH3 (k is 8–16), while their difference is that aviation kerosene is purer and contains minimal impurities. After entering the combustor, aviation kerosene is evaporated into gas-phase fuels and mixed with the air to undergo chemical reactions to release huge heat. In this way, a high-temperature and high-pressure fuel gas is produced, which is accelerated through the jet nozzle, thus producing the thrust needed.

90

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

The thrust of the engine is determined by the following thrust function F: F = mv ˙ + ρ1 σ1

(3.1)

where m˙ is the flow rate at the engine outlet; v is the velocity at the engine outlet; ρ1 denotes the outlet pressure; σ1 is the area of nozzle. In accordance with the thrust function, the flow rate, velocity at the outlet, and pressure in the combustor need to be increased in a bid to improve the engine thrust. Chemical reactions are fast in the combustor, which can be described by the one-step chemical reaction of fuels, as expressed by Eq. (3.2); or the two-step chemical reaction considering the intermediate product CO can also be used, as expressed by Eqs. (3.3) and (3.4). ( m) m O2 → nCO2 + H2 O Cn Hm + n + 4 2 (n m) m + O2 → nCO + H2 O Cn Hm + 2 4 2 1 CO + O2 → CO2 2

(3.2) (3.3) (3.4)

Gas compositions discharged from the jet nozzle of an airplane mainly consist of CO2 , H2 O, O2 , and N2 . Gas composition distribution at the outlet can be measured via experiments, or calculated through numerical simulation according to working conditions of the engine. Due to the high and stable O2 and N2 contents in the atmosphere, O2 and N2 discharged from the jet nozzle do not induce great disturbances to the atmosphere. The H2 O content in the atmosphere is rather unstable, so it is challenging to judge whether H2 O discharged from the jet nozzle disturbs the atmospheric H2 O content or not. Considering this, CO2 is selected to sense targets based on changes in the atmospheric CO2 concentration under disturbances.

3.1.1.3

Numerical Simulation of Disturbances of the CO2 Concentration Induced by Jet-Nozzle Flows

Numerical simulation of atmospheric disturbances induced by jet-nozzle flows was analyzed based on the theory of hydromechanics. Here, the classical theories and various equations in the hydromechanics were not explained, while only the detailed methods for numerical simulation were introduced. A relative coordinate system was adopted for computation. An airplane was fixed and the airflow velocity was set as the flying speed of the airplane. In this way, the unsteady problem that an airplane enters the target area in the computation is transformed into a steady problem in the relative coordinate system. The flying time of the airplane in the target measurement area can be calculated using the length of the area and the flying speed. The control-volume method (CVM) was adopted for difference discretization of the convection–diffusion equation. The Fluent software provides the difference equation of CVM for convection–diffusion problems on the basis of unstructured meshes.

3.1 Laser Detection on Atmospheric CO2 Disturbance

91

Various difference schemes from the first-order upwind format to the third-order monotone upwind scheme of conservation law (MUSCL) are available, which allow computation of incompressible to compressible supersonic flows. For computation of subsonic flows, the second-order quadratic upstream interpolation (for convective kinematics) (QUICK) was applied for the momentum equation and the pressure equation was solved using the Prestro method. Other scalars were calculated using the second-order upwind scheme. The methods were coupled and at the same time, governing equations including the continuity equation, momentum equation, energy equation, and component transport equation were solved. The iterative steps are shown as follows: (1) The fluid transport properties are updated on the basis of the current solution. (2) The continuity equation, momentum equation, energy equation, and component transport equation are solved simultaneously. (3) Scalars such as the turbulence quantity are calculated using updated numerical values of variables. (4) The improved numerical values of variables are used as initial values for the next iteration. The above steps are repeated until obtaining the convergence solution. When solving the general equation of hydromechanics, appropriate boundary conditions need to be provided. Details are as follows: (1) Inlet conditions: values of parameters such as the atmospheric velocity at the inlet, gas compositions, pressure, temperature, and turbulence quantity are given according to the flight height. For boundary conditions of the jet nozzle of the airplane, values of relevant parameters are given according to outlet parameters of the engine. (2) Boundary conditions of the upper, lower, and side faces of the computational domain: values of parameters including the velocity, gas compositions, pressure, temperature, and turbulence quantity are given on the far-field condition according to the flight height of the airplane and the size of the computational domain. (3) Conditions of the solid-wall surface: mesh nodes near the fuselage wall are processed using the wall-function method. (4) Outlet conditions: the outlet of the computational domain is far from the jet nozzle, to which the given pressure boundary condition is applied. When numerically solving the differential equations, the atmospheric parameters and jet-nozzle parameters of the engine need to be set according to the fight condition. In the computation, the atmospheric parameters include O2 mass fraction of YO2 = 0.22, CO2 mass fraction of YCO2 = 0.00042, water–vapor mass fraction of YH2 O = 0.019, static temperature of T = 236 K on the inlet section, and average pressure of P = 0.356 bar (1 bar = 0.1 MPa). The pre-processing software Gambit6.2 of Fluent was applied for effective division of the computational domain. The computational domain for atmospheric disturbance and diffusion induced by airplanes is large, for which the subregion meshing method was adopted: for the region near the airplane where parameters change

92

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

substantially, local mesh refinement was employed and the unstructured meshes were used; for the region far from the airplane where parameters change slightly, structured meshes were applied to reduce the computational workload. Due to the complex geometry of the fuselage, the research team simplified a certain type of real warcraft as a slender body. Such simplification only exerts slight influences on the disturbance and diffusion behind the warcraft. The computational domain was a cube measuring 250 m × 250 m × 2000 m. The computational domain for the inlet that needs mesh refinement near the fuselage had the dimensions of 40 m × 40 m; the size from the inlet of the computational domain to the fuselage head was 3 m. In this way, the warcraft was simplified into a slender body with the maximum diameter of 4 m and total length of 16 m. Because the warcraft was shown as a slender body, the regular hexahedral meshing could not be used. Considering the large computational space and the large number of meshes, the subregion meshing method was adopted. The space was divided into two parts: the subregion near the warcraft was divided using unstructured meshes, while regular hexahedral meshes were used in the subregion with small change gradients of parameters. In the computational process, meshes in the computational domain were refined and adjusted using the adaptive mesh refinement function based on parameter gradients in the flow field in software Fluent, so as to obtain better results. The computational domain contained 647,436 volume meshes and 1,343,527 surface meshes, with surface meshes used for transition between subregions. The mesh is shown in Fig. 3.6. Taking a certain type of warcraft as the example, atmospheric wind-field disturbances induced by the jet-nozzle flow of the warcraft under conditions with the flight Mach number of Ma = 0.8 and flight height of 8 km were numerically simulated. In the process, the background atmospheric wind velocity was simplified as zero. In this 200

60 150

40

100

20

H (m)

H (m)

50 0

0

-50

-20

-100 -150 -200

-40 -100

0

100

200

0

(a) Mesh of the inlet section

20

40

60

80

100

X (m)

X (m)

(b) Mesh refinement near the fuselage on the profile

Fig. 3.6 Mesh generation of the computational domain

3.1 Laser Detection on Atmospheric CO2 Disturbance

93

section, H, Z, and X in various figures separately represent the longitudinal, transverse, and axial directions. For the two columns of data in the right of each figure, the left column represents the gradient grade of wind velocity, while the other column indicates the specific values of wind velocities corresponding to each gradient. The measurement unit of velocity is m/s in various figures unless otherwise specified. Figure 3.7 illustrates distribution of the atmospheric velocities on the axial profile of the fuselage at different atmospheric turbulence scales. Near the fuselage, certain velocity distribution is also induced in both sides of the fuselage due to warcraft disturbances, as shown in Fig. 3.7a. It can be seen from Fig. 3.7a that the jet-nozzle flow is equivalent to a high-velocity jet flow that is slightly influenced by the fuselage and atmospheric turbulences. A large velocity gradient is observed near the jet nozzle, where the maximum velocity can reach 200 m/s. The atmospheric wind-velocity disturbance induced by the jet nozzle is mainly concentrated on the axial direction. The velocity attenuates fast behind the jet nozzle, and it decreases from 200 to 30 m/ s at a distance of 100 m (in Fig. 3.7a, the wind velocity beyond 80–100 m is 30 m/s; 200 m/s is obtained from the gradient value of 10 in the figure). 20

1

15

5

5

9

10

10

6

-5

5 547 8 6 5 2

6

0

55

H (m)

12 11 10 9 8 7 6 5 4 3 2 1

2

10

5

6 1

2

1

-10

1

3

9

-15 -20

0

20

40

x (m)

60

80

400 300 200 100 60 50 40 30 20 10 5 2

100

(a) Atmospheric turbulence scale of 0.2 m 20

1

-10 -20

2 2 59 4 5210 3 2 1

106 4 6910 96 3

9

6

0

5

H (m)

10

0

6

20

3

40

1 28

2

x(m)

2 1

60

1

4

80

100

12 11 10 9 8 7 6 5 4 3 2 1

400 300 200 100 60 50 40 30 20 10 5 2

(b) Atmospheric turbulence scale of 1 m 20

-10 -20 0

3

4

7

7

8

9 7

8

2 9 75

5

H (m)

10 0

12 11 10 9 8 7 6 5 4 3 2 1

1

3

3 1

7

5

2

2

4

9

6

6

8

1

1 20

40

60

80

400 300 200 100 60 50 40 30 20 10 5 2

100

x (m)

(c) Atmospheric turbulence scale of 10 m Fig. 3.7 Distribution of atmospheric velocities along the axial profile of the fuselage at different atmospheric turbulence scales

94

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

To study the transverse diffusion of atmospheric disturbances induced by the jetnozzle flow, the cross section of the jet nozzle was taken and several measuring points in the Z direction 0, 5, 10, and 15 m from the axis were selected. Figure 3.8 illustrates changes in the velocity induced by the jet-nozzle flow at various measuring points with time. It can be seen from the figure that the velocity on the axis has decreased to 0 after 4 s. The peak velocity at the location 5 m from the axis is only 4 m/s, while the velocity varies more unobviously when the distance from the axis is longer than 10 m. Therefore, the atmospheric wind-velocity disturbances induced by the jet-nozzle flow are low in the transverse direction, and they are attenuated fast in this direction. Taking a certain type of warcraft as an example, the jet nozzle of the engine is given by referring to parameters of the jet nozzle of this type of warcraft: O2 mass fraction of YO2 = 0.02, CO2 mass fraction of YCO2 = 0.16, water–vapor mass fraction of YH2 O = 0.12, mean temperature of T ∗ = 2065.0 K on the cross section, average pressure of P = 3.056 bar, and total mass flow-rate of m˙ = 60 kg/s. It is 5

600

velocity-magnitude (m/s)

velocity-magnitude (m/s)

4

400

200

0

0

10

20

30

40

3

2

1

0

t (s)

0

10

40

(b) 5 m from the axis

5

5

4

4

velocity-magnitude (m/s)

velocity-magnitude (m/s)

30

t (s)

(a) On the axis

3

2

1

0

20

0

10

20

30

40

t (s)

(c) 10 m from the axis

3

2

1

0

0

10

20

30

40

t (s)

(d) 15 m from the axis

Fig. 3.8 Changes in the atmospheric wind velocity induced by the jet-nozzle flow at various measuring points in the transverse direction with time

3.1 Laser Detection on Atmospheric CO2 Disturbance

95

assumed that the CO2 concentration in the background atmosphere at the altitude of 8 km is 300 × 10–6 , that is, the molar concentration is about 5 × 10–6 kmol/m3 . On this condition, the disturbance of the atmospheric CO2 concentration induced by the jet-nozzle flow of the warcraft at the flight height of 8 km was numerically simulated. In the section, H, Z, and X in various figures separately represent the longitudinal, transverse, and axial directions. For the two columns of data in the right of each figure, the left column represents the gradient grade of the CO2 concentration, while the other column indicates specific concentrations corresponding to each gradient. The measurement unit of concentration is kmol/m3 in various figures unless otherwise specified. Figure 3.9 illustrates the profile distribution of CO2 concentrations at different atmospheric turbulence scales. Figure 3.10 provides the distribution of CO2 concentrations on the cross section 2000 m behind the jet nozzle. It can be seen from Fig. 3.10 that, the jet-nozzle flow at a distance of 2000 m still can induce strong disturbances to the atmospheric CO2 concentration. The greater the atmospheric turbulence scale is, the faster the diffusion of the concentration disturbance. As displayed in Fig. 3.10, the maximum CO2 concentration is about 9 × 10–6 kmol/m3 on the distant cross section that is 2000 m from the jet nozzle. Therefore, the jet-nozzle flow still can induce 4 × 10–6 kmol/m3 of disturbance to the CO2 concentration at a distance of 2000 m after excluding the background atmosphere CO2 concentration. Through theoretical analysis of hydromechanics, software Fluent was adopted for numerical simulation of atmospheric wind-field disturbances and disturbances of the atmospheric CO2 concentration induced by the jet-nozzle flow of a certain type of warcraft with the flight Mach number of Ma = 0.8 and flight height of 8 km. The following conclusions were obtained: the jet-nozzle flow is equivalent to a highspeed jet flow that is slightly affected by the fuselage and atmospheric turbulences. The velocity gradient near the jet nozzle is large and the maximum velocity can reach 200 m/s. The atmospheric wind-speed disturbances induced by the jet-nozzle flow are mainly concentrated on the axial direction. The velocity attenuates fast axially behind the jet nozzle and it has attenuated to 30 m/s at a distance of 100 m and to 0 after 4 s. The jet-nozzle flow induces slight disturbances to the transverse atmospheric wind velocity. The diffusion of the jet-nozzle flow on the cross section that is 2000 m from the jet nozzle still can cause strong disturbances of 240 × 10–6 to the CO2 concentration. The greater the atmospheric turbulence scale is, the faster the diffusion of concentration disturbances. Because the atmospheric windspeed disturbances induced by the jet-nozzle flow are attenuated fast and low in the transverse direction, they are unfavorable for early warning and detection of targets. In comparison, the disturbances of the CO2 concentration induced by the jet-nozzle flow diffuse to a long distance and are large in the transverse direction, which is conducive to the early warning and detection of targets.

96

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

H (m)

20

10

1

0

2 30

7

1

2

4

6 4

23

-10

-20

500

1000

1500

2000

11 10 9 8 7 6 5 4 3 2 1

0.0013 0.0005 0.0002 0.0001 5E-05 2.7E-05 2E-05 1E-05 8E-06 6E-06 5E-06

x (m)

(a) Without atmospheric turbulences 20

7

6 1

H (m)

2 4

5

64

4

4

5

0

11 10 9 8 7 6 5 4 3 2 1

1

10

3

-10

1

-20

200

400

600

800

1000

1200

1400

1600

1800

0.0013 0.0005 0.0002 0.0001 5E-05 2.7E-05 2E-05 1E-05 8E-06 6E-06 5E-06

2000

x(m)

(b) Atmospheric turbulence scale of 1 m 20 10

H (m)

1

2

0

4

2 2

44

1

5

2

-10 -20

11 10 9 8 7 6 5 4 3 2 1

1

200

400

600

800

1000

1200

1

1400

1600

1800

0.0013 0.0005 0.0002 0.0001 5E-05 2.7E-05 2E-05 1E-05 8E-06 6E-06 5E-06

2000

x (m)

(c) Atmospheric turbulence scale of 10 m Fig. 3.9 Profile distribution of CO2 concentrations at different atmospheric turbulence scales

3.1.2 Laser Detection Principles of the Atmospheric CO2 Disturbances A lidar uses laser as the light source. After interactions with atmospheric media, light waves produce radiation signals containing relevant information of gas atoms, gas molecules, and aerosol particles. Through inversion, the information of the gas atoms, gas molecules, and aerosol particles can be obtained. Therefore, the technological base of lidars is various physical processes caused by interactions of light radiation with atoms, molecules, and aerosols in the atmosphere. Due to different targets of remote sensing and different radiation signals to be measured, various types of lidars have been produced.

3.1 Laser Detection on Atmospheric CO2 Disturbance

-15 -20 -20

-10

-10

0

10

1.1E-05 1E-05 9E-06 8E-06 7E-06 6E-06 5.5E-06 5.2E-06 5E-06

0

2

0

-10

2

10

3

10

20

6

1.1E-05 1E-05 9E-06 8E-06 7E-06 7E-06 5.5E-06 5.2E-06 5E-06

5

4

4

3 2

1

Z (m)

15

9 8 7 6 5 4 3 2 1

20

1

4

8

-10

10

(b) Atmospheric turbulence scale of 0.2 m

5

2

-20 -20

2 1

7 6

5

3 2

-15

5 3

0

56

4

1

-10

-5

6

3

H (m)

2

0

-5

1

5

-15 -10

Z(m)

H (m)

1

2 3 4

64

12

2

9 8 7 6 5 4 3 2 1

20 1

-20-20

20

(a) Without atmospheric turbulences

10

6

-15

Z (m)

15

6

1

3

7

-5

4 5 2 1

-10

0

7 5 32

3

6 8

3

98 785 3 4 21

-5

2 1 54 7 8

5

2 41

1

10

1.1E-05 1E-05 9E-06 8E-06 7E-06 6E-06 5.5E-06 5.2E-06 5E-06

2

6

2

0

3

53 8

4

15

4

5

H (m)

1

9 8 7 6 15 4 73 2 1

20

2 3 88

10

1.1E-05 1E-05 9E-06 8E-06 7E-06 6E-06 5.5E-06 5.2E-06 5E-06

2 3

15

9 8 7 6 5 2 41 43 654 2 87 1

H (m)

20

97

20

(c) Atmospheric turbulence scale of 1 m

-20 -20

-10

0

10

20

Z(m)

(d) Atmospheric turbulence scale of 10 m

Fig. 3.10 Cross-sectional distribution of CO2 concentrations at different atmospheric turbulence scales (X = 2000 m)

3.1.2.1

Laser-Atmosphere Interaction

A lidar inverts atmospheric physical quantities according to backscattered signals of the laser. According to the classical theory, light scattering is caused by scatterers such as gas molecules and aerosols in the atmosphere. Under laser irradiation, the scatterers are polarized to induce oscillating electromagnetic multipoles under the action of the laser electromagnetic field. Due to electromagnetic oscillations, these scatterers radiate electromagnetic waves (EMWs) outwards, and these outward radiating EMWs are light scattering. Many atmospheric molecules have absorption spectra, and the absorption spectra of molecules can be explained by the quantum theory. Absorption spectra show the pressure broadening and Doppler broadening effects. Raman scattering and resonance scattering of atmospheric molecules both can only be explained by the quantum theory. In Raman scattering, photons undergo inelastic collisions with microscopic particles, thus radiating a scattered photon that is different from the incident photon in energy and direction. At the same time, it induces the transition from the initial state to the terminal state of particles.

98

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

In the theory of light scattering, the relationship between the scatterer size and light wavelength is represented by the scale parameter ρ, which is defined as ρ = 2πa/λ, where a is the scatterer radius and λ is the light wavelength. According to the difference in the scale parameter ρ of particles in the atmosphere, light scattering can be divided into Mie scattering and Rayleigh scattering. According to the difference between photons and particles in energy exchange, it can be divided into elastic scattering and inelastic scattering. When transmitted in atmospheric media, laser may undergo multiple scattering processes, including Rayleigh scattering by molecules and small-scale aerosol particles, Mie scattering by large-scale aerosol particles, Raman scattering with varying scattering frequency, and resonance scattering with scattering intensity being several orders of magnitude higher than that of Rayleigh molecular scattering. Moreover, the atmospheric molecules have wide electronic absorption bands and molecular vibration-rotational spectra from ultraviolet to infrared (IR) bands. During transmission in the atmosphere, the laser with wavelength overlapped with absorption lines of some molecules in the atmosphere will be strongly absorbed by the molecules. When the laser is transmitted in the atmospheric media, various physical processes including molecular scattering, large-particle scattering, Raman scattering, and absorption have their own characteristics. Table 3.2 lists values of typical cross sections under action of the interaction between laser and atmospheric media. Table 3.2 Values of cross sections under action of the interaction between laser and atmospheric media Action processes

Media

Wavelength relationships

Cross sections under action (cm2 /sr)

Detectable atmospheric compositions

Rayleigh scattering

Molecules

λt = λr

10–27

Atmosphere density, temperature, and gravity waves

Mie scattering

Aerosols

λt = λr

10–26 to 10–8 Aerosols, smoke plumes, and clouds

Raman scattering

Molecules

λt / = λr

10–30

Humidity, trace gas species at close range, atmosphere density, and temperature

Resonance scattering

Atoms and molecules

λt =λr

10–23 to 10–14

High-altitude metal atoms and ions such as Na+ , K+ , Ca+ , and Li

Fluorescent scattering

Molecules

λt / = λr

10–25 to 10–16

Organic molecules

Absorption

Atoms and molecules

λt = λr

10–21 to 10–14

Trace gas species (O3 , SO2 , and CO2 )

Doppler effect

Atoms and molecules

λt / = λr

Wind velocity and wind direction

Note λt is the wavelength of emitted laser; λr is the wavelength of received light

3.1 Laser Detection on Atmospheric CO2 Disturbance

3.1.2.2

99

Lidar Equation for Atmospheric Detection

The mathematical expression that describes the relationship between the echo signal intensity and various factors is called the lidar equation. The echo intensity of a lidar is related to many factors, which can be roughly divided into three types: firstly, a lidar has to use a certain atmosphere-laser interaction mechanism to produce lidar echoes; secondly, no matter what echo mechanism is used, the emitted light and echoed light of the lidar are attenuated by the atmosphere when they are transmitted in the atmosphere; thirdly, the echo signal intensity of the lidar is also closely correlated to numerous factors in terms of lidar configurations and technologies. Therefore, the lidar equation can be written as follows: ⎡ ⎤ {R P0 (λ)Aβ(λ, R)ΔR exp⎣−2 α(λ, z)dz ⎦ P(λ, R) = C R2

(3.5)

0

where P(λ, R) is the power of echo signals from an atmospheric segment at the altitude of R ∼ R + ΔR received by the lidar; C is the correction constant of the lidar; P0 (λ) is the power for emitting laser beams; A is the light-receiving area of the receiving telescope; R is the detection range (or altitude); β(λ, R) is the backscattering coefficient of a detected atmospheric composition; α(λ, z) is the total extinction coefficient of the atmosphere. Different radars may feature different scattering types and correspondingly, they have different β(λ, R). To more clearly reveal influences of various factors on the lidar echo signals, Eq. (3.5), namely, the lidar equation can be written as a product of two terms, that is, P(λ, R) = A1 · B 1 . Therein, A1 = C P0 (λ)AΔR/R 2 is a quantity that is only related to lidar parameters and therefore is termed as the radar item; {R B 1 = β(λ, R) exp[−2 0 α(λ, z)dz] is a quantity that is only related to atmospheric parameters and is dubbed as the atmospheric item. In the radar item A1 , the laser emission power P0 (λ) and the area A of the receiving telescope exert obvious influences on the lidar echo signals. The larger the P0 (λ) and A are, the greater the echo power P(λ, R) received by the lidar. This is why the product P0 (λ) · A (unit: W m2 ) is commonly called the quality factor. The detection range appears as R 2 in the denominator of the radar item, which means that the lidar echo signal is inversely proportional to the square of detection height even under conditions of uniform atmosphere and without attenuation. Such attenuation with the square of detection range occurs because the solid angle of the detected atmosphere stretched for the receiving telescope decreases with the altitude, while it is irrelevant to the intensity of atmospheric scattering. ΔR is the range resolution of detection and its minimum value is dependent on the pulse width τ of emitted laser, that is, ΔRmin = cτ/2, where c is the velocity of light. For most pulsed lasers that are applied to lidars, the pulse width is generally at the order of magnitude of 10–8 s while ΔRmin is only several meters. Therefore, ΔR in practical application is generally determined by the sampling gate width (larger than τ ) in the detection

100

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

circuit of echo signals. The correction constant C in the radar term can be further written as C = ε(R)·η, where ε(R) is the overlap factor of the lidar, which represents the overlapping degree between the emitted laser beam and the field of view (FOV) of the receiving telescope and it is 1 in the case of complete overlapping; η is the efficiency of the system. In the atmospheric term B 1 , the backscattering coefficient β(λ, R) (unit: cm−1 sr−1 ) represents the scattering capacity of the atmosphere for the laser at the range of R and it can be further written as β(λ, R) = N (R) · σ (λ). Therein, N (R) is the density of detected atmospheric compositions (unit: cm−3 ), which is generally a function of the range R; σ (λ) is the backscattering cross section of the detected atmospheric compositions (unit: cm2 sr−1 ). β(λ, R) is a physical quantity that represents the echo generation mechanism in the lidar equation, which means that the echo signal intensity is directly proportional to the density and backscattering cross section of detected atmospheric compositions. This is also the cause why the density of detected {R atmospheric compositions can be inverted by lidar echoes. exp[−2 0 α(λ, z)dz] indicates the attenuation of light when it is transmitted twice in the detection range R from the lidar. Obviously, to reduce such attenuation, an appropriate laser emission wavelength needs to be selected to further minimize the extinction coefficient α(λ, z). Although the extinction coefficient α(λ, z) is contributed by both scattering and absorption, atmospheric absorption is commonly more important. Therefore, except for the lidar based on the absorption mechanism, the emission wavelength of other lidars is mainly in the atmospheric window to avoid intense atmospheric absorption. The avoidance of extinction due to scattering is generally complex. This is because in many lidars, scattering, as a kind of extinction, should be avoided; while as a mechanism to produce radar echoes, it also needs to be harnessed. The selection of the laser emission wavelength should follow this general principle: when atmospheric scattering changes due to variation of the laser wavelength, the wavelength selection should be conducive to enhancing the overall detection capacity of lidars.

3.1.2.3

Principle of DIAL Detection of Atmospheric CO2

After combusting hydrocarbons, targets including airplanes discharge exhaust that is mainly composed of H2 O and CO2 , while the CO2 concentration in the background atmosphere remains relatively stable. So, it is CO2 in the exhaust that remarkably disturbs the surrounding atmosphere, and a target can be sensed by detecting variation of the CO2 concentration in different ranges and directions. Because DIALs have a high detection SNR, a range-resolution DIAL system can be used to detect the CO2 concentration. The principle of the DIAL is displayed in Fig. 3.11. After being emitted by a lidar transmitter, a laser pulse is scattered by aerosols in the atmosphere and the scattered echoes are detected by a receiver. After signal processing, the concentration of gases to be detected can be obtained. During transmission in the atmosphere, the laser pulse on the one hand is scattered by aerosols, while on the other hand, it is

3.1 Laser Detection on Atmospheric CO2 Disturbance

101

Fig. 3.11 Schematic diagram for the DIAL

absorbed by atmospheric matter. The intensity of absorption signals reflects the CO2 concentration. To excluding influences of other factors as far as possible and obtain accurate CO2 concentration, two emitted laser beams with similar wavelengths are used in the system. One wavelength is at the center of the CO2 absorption peak and recorded as λon ; the other is outside the absorption peak and recorded as λoff , so that the beam is slightly absorbed. It is assumed that the pulse width of the laser pulse is τL , emission power is P0 , and detection range is R. The equation for lidar echoes can be expressed as [4] ⎡ P(R) = P0 · (cτL /2)β(R)AR R −2 exp⎣−2

{R

⎤ α(r )dr ⎦

(3.6)

0

where c is the velocity of light; β(R) is the backscattering coefficient in the atmosphere; A R is the effective area; α(r ) is the extinction coefficient of the atmosphere and α = σ N + ε, in which σ N is the extinction coefficient caused by CO2 absorption (σ is the absorption cross section and N is the CO2 concentration) and ε is the extinction coefficient excluding CO2 absorption and is mainly attributed to other interfering gases. For the range-resolution DIAL, two adjacent range intervals represented by 1 and 2 are considered. Light sources λon and λoff are rewritten as Eq. (3.6), thus obtaining four echo equations of the lidar. Generally, it is regarded that in the range unit (R1 , R2 ), the CO2 concentration N and the differential-absorption cross section Δσ of light sources λon and λoff are constants. By solving the echo equations, the CO2 concentration is obtained as

102

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

⎤ ⎡ ) ( ) {R2 ( β P β 1 P ⎣ln off,2 on,1 + ln on,2 off,1 + (εon − εoff )dr ⎦ N (R1 , R2 ) = 2Δσ ΔR Pon,2 Poff,1 βon,1 βoff,2 R1

(3.7) where Δσ = σ on − σoff is the differential-absorption cross section; ΔR = R2 − R1 is the spatial sampling interval; Pon, j and Poff, j ( j = 1, 2) separately are laser echo signals of light sources λon and λoff at R1 and R2 ; βon, j , βoff, j , εon, j , and εoff, j separately denote the backscattering coefficients and extinction coefficients of light sources λon and λoff . Because λon and λoff are very close, then βon, j = βoff, j and εon, j = εoff, j . Equation (3.7) is simplified as N (R1 , R2 ) =

) ( Poff,2 Pon,1 1 ln 2Δσ ΔR Pon,2 Poff,1

(3.8)

This is the principle of detecting the CO2 concentration using the DIAL. Because the intrinsic electric dipoles of CO2 are polar molecules, they have strong vibration–rotation absorption bands in the IR spectral region. The spectral lines are narrow in these spectral bands and distributed at 2.7, 4.3, and at 1.2, 1.43, 1.6, 2.0, 5.2, 10.4, and 15 μm in the mid-far-IR and near-IR regions, as illustrated in Fig. 3.12 [5]. Therein, the band at 2.7 μm is a strong absorption band and consists of two absorption bands with the central wavelengths separately of 2.69 and 2.77 μm. When detecting CO2 using an unloaded actinometer, three spectral bands 1.6, 2.0, and 4.3 μm are mainly selected. Differential-absorption radars have been applied to detect gases such as O3 , NO2 , and SO2 , while their application to CO2 detection still faces many difficulties. This is mainly because ➀ the absorption spectra are in the IR band, while there is no readymade system with such light sources is available; ➁ the absorption lines are narrow, so the absorption line width not only needs to be compressed for ON-line light sources, frequency stabilization and frequency locking are also needed; ➂ the most mature detection devices at present are photomultiplier tubes (PMTs) and silicon avalanche diodes (SADs), which are not suitable for the IR band beyond 1.1 μm; ➃ during CO2 detection using a differential-absorption radar, H2 O is the main interfering gas. Fig. 3.12 Absorption spectrograms of CO2 and H2 O

3.1 Laser Detection on Atmospheric CO2 Disturbance

103

Considering the above factors, the differential-absorption bands studied are mainly 1.6 and 2.0 μm. This indicates that the existing lidar systems and methods cannot be copied for CO2 detection, and the lidar detection systems for atmospheric CO2 should be further studied.

3.1.3 DIAL Detection System Schemes The lidar system used for atmospheric detection generally consists of a laser source, a receiving telescope, a background filter, a photoelectric detector, a signal preamplifier, and a signal pickup assembly. Figure 3.13 illustrates the block diagram for the general constitution of a lidar system. A lidar system can be composed either of a single light source and a single-channel signal receiving device, or of multiple light sources and multiple sets of signal receiving devices. The lidar works in the following process: when it is transmitted in the atmosphere, a laser beam is scattered after being encountered with scatterers (atmospheric molecules, aerosols, and clouds). The backscattered light returns to the lidar and is received by the receiving system. By inverting the received signals, the needed atmospheric physical quantities can be obtained. According to the detection modes of laser echoes, there are two detection systems: one is the incoherent detection system and the other is the coherent detection system. The structure of the incoherent detection system is relatively simple, for which mainly two modes are widely used: one is to use a high-energy pulse to emit two differential laser beams, which are received by a PMT or an avalanche photon diode (APD), and the detection data are accumulated for a long time to improve the SNR. This mode can receive the backscattered echoes of the atmosphere and has certain spatial resolution, while the detection range is not far limited by the laser energy and time resolution. The other is to emit laser beams using amplitude-modulated continuous waves, and different low-frequency modulations are used for the ON-line and OFFline lasers. This mode generally needs to use diffusing objects such as the ground

Fig. 3.13 Block diagram for the general constitution of a lidar system

104

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

features as the cooperative targets and does not have range resolution. The results obtained are average column concentrations within the detection range. The typical representatives of the two modes are the pulsed DIAL of the NASA and the amplitudemodulated continuous-wave lidar developed by Japanese researchers. Compared with the incoherent detection, the coherent detection of echoes has been widely applied to fields such as wind-field detection, velocity detection, detection of trailing vortexes of airplanes, and range detection. Strong local-oscillator (LO) light is introduced in coherent detection, which can effectively realize the detection of weak optical signals at the order of magnitude of pW. Coherent detection, as a holographic detection mode, can obtain data of the amplitude, frequency, polarization, and phase at the same time. Coherent detection can be divided into two types: one is homodyne detection, which is significantly affected by the phase noise between signal light and LO light. Added with the low-frequency noise interference in the detection circuit, it is unfavorable for the accurate extraction of the signal amplitude. The other is heterodyne detection, which shifts the coherent information from the zero frequency to a beat frequency much lower than the light frequency, thus improving the antijamming capability of systems. Here, the heterodyne detection of echoes is studied with particular attention.

3.1.3.1

Detection System for Background Atmosphere CO2 Concentration

A DIAL system for receiving hard target echoes is introduced in the section [5], which has been used for around-the-clock monitoring of changes in the atmosphere CO2 concentration. The system separately modulates the laser intensity within and beyond the absorption peak of CO2 and uses the single-frequency detection technology to extract echo signals. By using an all-fiber configuration, the system has a reliable structure and can be moved conveniently. The structure of a continuous-wave modulation DIAL system is displayed in Fig. 3.14. The wavelengths of ON-line and OFF-line wavelength lasers are separately modulated using their individual wavelength control units, and the electrooptical modulator is used for intensity modulation at different frequencies for the two laser beams. A part of output light is photovoltaically converted by the detector, and the signal is adopted as the bias control signal for the modulator; the remaining light is coupled and enters the light amplifier, the outgoing light from which is partially reflected by a reflector for energy monitoring. This method can avoid the energy monitoring error caused by instable polarization of outgoing light from erbiumdoped fiber amplifiers (EDFAs). The remaining outgoing light from the amplimer enters the atmosphere. The telescope then receives the hard target echoes, which are focused on the fiber, pass through the filter, and then received by the detectors. Afterwards, the analog–digital (A/D) acquisition card is used to collect the emitted energy monitoring signal and the echo signal, so as to invert the CO2 concentration.

3.1 Laser Detection on Atmospheric CO2 Disturbance

105

Fig. 3.14 Schematic diagram for the structure of a continuous-wave modulation DIAL system

(1) ON-line and OFF-line wavelength light sources When deducing the CO2 concentration, the extinction coefficients excluding CO2 absorption are regarded as approximately same. However, it is not the case in practice. Such a difference may incur an error to the system, in which influences of the water vapor, pressure, and temperature on the extinction coefficient are mainly considered. Previous research has shown that when the frequency stability of ON-line wavelength lasers is superior to 0.1 pm (rms) and that of OFF-line wavelength lasers is superior to 0.4 pm (rms), the influence of wavelength instability can be ignored even if the measurement precision reaches 1 × 10−6 [6]. Therefore, the ON-line wavelength is actively locked using the frequency-offset-locking method, combining with relevant detection technologies [7]. For OFF-line wavelength lasers, they are driven by the self-made constant-temperature cross-flow controller. The test results for frequency stability of ON-line and OFF-line wavelength lasers are illustrated in Fig. 3.15. (2) Frequency modulation and single-frequency detection Noises in the system (such as the background light noise and circuit inherent noise) are mainly low-frequency noises or uniformly distributed white noises with a wide frequency range. Due to the high gain of echo detectors, these noises have a large amplitude in the time domain. The use of the single-frequency detection technology can avert the noise beyond the modulation frequency, which is equivalent to an extremely-narrow-band filter that can effectively inhibit the out-of-band noise and improve the SNR. (3) Bias control of the electrooptical modulator Because the system needs to work for a long time while internal crystal properties of the electrooptical modulator may change with time, the bias voltage that modulates and generates sine waves varies. It may distort the waveform in serious cases (for

106

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

Fig. 3.15 Test results for frequency stability of the ON-line (a) and OFF-line (b) wavelength lasers

example, appearance of flat-topped or flat-bottomed waveforms), which is likely to incur errors in spectrum sensing, so bias control is very necessary. If setting the lowest point of output sine waves as the zero point, the waveform may be distorted under any slight inadequate control. Therefore, the lowest point of sine waves should be controlled at an appropriate value. Figure 3.16 shows the bias control results. By using the designed experimental system, multiple batches of long-time monitoring experiments on the CO2 concentration were carried out, as shown in Fig. 3.17, which provides the measurements of changes in the atmospheric CO2 concentration in three days, namely, October 18, November 16, and November 17, 2013. It can be seen that the CO2 concentration at sunrise in the three days all declined because human activities were not frequent and exerted low influences on the atmospheric CO2 concentration, which was mainly affected by plant photosynthesis. The postmeridian atmospheric CO2 concentration always rose, which might be a result of the remarkable influences of human activities on the atmospheric CO2 concentration. In the nighttime, the atmospheric CO2 concentration changed greatly, which might Fig. 3.16 Stability results under bias control

3.1 Laser Detection on Atmospheric CO2 Disturbance

107

Fig. 3.17 Monitoring results of changes in the atmospheric CO2 concentration in three days in Hongkou District, Shanghai in 2013

be partially because changes in the atmospheric CO2 concentration in the nighttime are mainly influenced by air motion in local and surrounding regions, which leads to certain uncertainty.

3.1.3.2

All-Fiber Coherent CO2 Detection System

The designed detection system has a laser band of 1.6 μm, which is within the CO2 absorption lines and can realize differential-absorption detection of CO2 . In addition, 1.6 μm is also in the main bands of fiber communication, for which relevant devices are characterized by mature development, low cost, and high integration. Besides, the band is also safe for human eyes. In view of this, a 1.6-μm all-fiber laser coherent CO2 detection system was designed in the section, and the system structure is shown in Fig. 3.18. In Fig. 3.18, the emission system uses an external cavity diode laser (ECDL) as the master oscillator for light sources λon /λoff . To guarantee stability of the light output by the ECDL, some light is sampled to pass through the wavemeter (a standard gas cell filled with CO2 is adopted as the wavemeter), so as to monitor its wavelength changes. The feedback signal of wavemeter allows one to adjust the wavelength, so that the wavelength is stabilized on the absorption peak of CO2 . After being

108

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

Fig. 3.18 Block diagram for the structure of the all-fiber laser coherent CO2 detection system

modulated, the wavelength of ECDL can be adjust outside the absorption peak to serve as the light output of the λoff . In the system, what is output by the ECDL is the continuous-wave laser. After applying pulse signals to the acousto-optic modulator using the pulse signal generator, the continuous-wave laser turns to be pulsed laser after passing through the modulator. To avoid interference between continuous pulse-echo signals, the emitted pulses should not be too dense, so the repetition frequency should not be too high. Based on the working mechanism of time division multiplexing, the laser signal is emitted outward through the isolator after power amplification using the EDFA. After the echo signal is received by the telescope, the sky background noise therein is filtered by using a narrow-band filter. A fiber coupler is adopted to sample the ONline and OFF-line light sources to serve as LO signals after being transmitted via a long fiber, followed by frequency mixing separately with ON-line and OFF-line echo signals. InGaAs PIN diodes are used in the coherent detector. The length of the long fiber should be roughly identical to the detection range, so as to enhance coherence of the two signals. The mixed signals are subjected to digital processing after amplification, filtering, data acquisition, and then can be used to invert the CO2 concentration. (1) High-resolution wavelength control The emission system uses the ECDL as the master oscillator for light sources λon / λoff . As shown in Fig. 3.19, the laser is mainly composed of a tube of the diode laser, a collimator lens, a diffraction grating, and a rear mirror. One end of the diode laser is plated with a complete-reflective film and the other end is plated with a semi-reflective film. The complete-reflective film and the rear mirror form a resonator. The grating is not only an optical frequency selective element but also an optical feedback element, and it plays a role in compressing the line width. Lasers of different wavelengths have different diffraction angles on the grating, which enables frequency selection. By controlling moving parts of the DC motor, the cavity length is varied, thus realizing coarse tuning of the laser wavelength. The fine tuning of the laser wavelength is achieved by changing the angle of the rotating mirror after driving the piezoelectric

3.1 Laser Detection on Atmospheric CO2 Disturbance

109

Fig. 3.19 Schematic diagram for wavelength modulation of the ECDL

ceramics (PZT). The parts for coarse tuning and fine tuning of the laser wavelength are separately driven by controlling the external current input. The main task of high-precision wavelength control is to stably control the detection laser wavelength separately at the center of and outside the spectral absorption peak of detected gases, so as to reach the goal of differential-absorption detection. Therefore, the control of laser wavelength is closely related to the fine absorption spectra of detected gases. The fine spectrogram for transmittance of the detection object (CO2 ) of the detection system of interest is illustrated in Fig. 3.20: there are 4–5 absorption lines of CO2 in every nanometer of the spectrum, and the absorption lines are narrow. Hence, when detecting CO2 using the lidar, the laser needs to have a very narrow line width and its wavelength should also be precisely tunable. Such lasers are generally expensive and face difficulties in wavelength control. The high-precision wavelength control has become one of the key technologies of the system. Fig. 3.20 Absorption lines of CO2 near 1.6 μm

110

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

The high-precision wavelength control method used in the detection system is described as follows: a sinusoidal signal (or triangular wave signal) is used to modulate the intensity signal of lasers and then the modulated signal is demodulated. As shown in Fig. 3.21a–c, when the modulated signal is far from the absorption peak, the demodulated signal has a high amplitude; when the modulated signal is near the absorption peak, the amplitude of the demodulated signal is low; if the modulated signal is just on the absorption peak, the amplitude of the demodulated signal is minimum. Figure 3.21d depicts the change curve for the derivative ΔIT of the intensity signal IT of laser passing through absorption spectrum with the laser center frequency ν. Assuming that the laser center frequency is υ, laser intensity is IT , the amplitude of the modulated sinusoidal signal is m (m is generally very small), and the frequency of the modulated sinusoidal signal is Ω, then there is the following relation: IT (υ) = IT (υ + m sin(Ωt))

(3.9)

In Eq. (3.9), it requires that Ω < Γ, where Γ is the line width of absorption lines. Through Taylor series expansion of Eq. (3.9), there is ( 2 2 ) m sin Ωt d2 IT dIT + IT (υ + m sin(Ωt)) = IT (υ) + (m sin Ωt) dυ 2! dυ 2 ) 3 ( 3 3 [ ] m sin Ωt d IT m 2 d2 IT + + · · · = IT (υ) + + ··· 3! dυ 3 4 dυ 2 ] [ m3 d 3 IT dIT + sin2 Ωt + · · · + sin Ωt m dυ 8 dυ 3 ] [ m 2 d2 I T + ··· + ··· (3.10) + cos 2Ωt − 4 dυ 2 2

2

Under the working mechanism of frequency modulation, IT (υ) + m4 ddυI2T + · · · in Eq. (3.10) is a DC component, which corresponds to the DC part of the modulated signal added. Different DC components correspond to different central wavelengths of the laser. In Eq. (3.10), the first-order harmonic coefficient is 3 3 m dIdυT + m8 sin2 Ωt ddυI3T + · · · . Because the amplitude m of the modulated sinusoidal signal is low and the high-order term can be ignored, the first-order harmonic coefT of intensity ficient can be simplified as m dIdυT , including the first-order derivative dI dυ for the center frequency, that is, the corresponding curve in Fig. 3.21d. By detecting the first-order harmonic coefficient using the lock-in amplifier, the result can serve as the feedback signals for driving control of the PZT for fine tuning of the wavelength. According to the above analysis, the design scheme for wavelength control is proposed, as shown in Fig. 3.22. In Fig. 3.22, a DC voltage signal is first provided to the driving controller of the ECDL and then modulated by generation of a sinusoidal signal by the wavegenerating circuit. The moving part of the DC motor is controlled to change the

3.1 Laser Detection on Atmospheric CO2 Disturbance

111

Fig. 3.21 Schematic diagram for wavelength control

cavity length, so as to achieve coarse tuning of the laser wavelength; afterwards, feedback is provided by detecting the signal intensity of the detector to control the value of DC voltage signals, so that the wavelength is stabilized at an absorption peak. Then, the lock-in amplifier is adopted to extract the first-order harmonic coefficient from the detector signal, which is used as the feedback signal after A/D conversion.

112

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

Fig. 3.22 Design scheme for wavelength control

The angle of the rotating mirror is changed by driving the PZT to realize fine tuning of the light frequency, so that the wavelength is finally stabilized on the absorption peak. The control algorithm can be controlled using the traditional proportionalintegral-derivative (PID) controller. (2) Coherent detection of echoes In the design of a detection system, the detection of weak laser echo signals in the atmosphere is a technological difficulty. The research team adopted a coherent detection system with advantages in greatly improving the SNR, suppressing the background noise, and improving the detection precision for CO2 . The coherent detection system can also detect the Doppler shift of echo signals and the wind. When designing the detection system, the laser detection band of 1.6 μm is used, which enables one to harness lots of mature devices for fiber communication. This idea has also been applied to coherent detection of echoes to design the coherent detection system based on relevant concepts of receivers of fiber communication. As mentioned above, coherent detection can be divided into homodyne and heterodyne detection systems. Therein, the homodyne detection system requires frequencies of the laser echo signal and LO signal to keep constant persistently, which is difficult to realize in practical atmospheric detection. In comparison, heterodyne detection needs different frequencies of echo light and LO light, so that they have a certain frequency difference. The frequency difference can be achieved by frequency shift of the laser using the acousto-optic frequency shifter. Moreover, AC coupling can be used in the front amplifier circuit due to use of heterodyne detection, which reduces the error incurred by the operating point drift of the circuit. To decrease the noise effect of the LO signal, a balanced detector can be employed to detect the input light signals, which greatly reduces the LO noise and improves the system SNR. In the coherent detection system, the LO signal and echo signal are mixed in a fiber coupler. The fiber coupler is a four-port device, in which two ports are separately used for inputting the echo signal and LO signal; while the other two ports output the mixed signal of LO light and echo light. For a 50/50 light coupler, 50% of energy separately of the LO signal and echo signal is sent to the output ports. For the fiber

3.1 Laser Detection on Atmospheric CO2 Disturbance

113

[

] 1 1 coupler of 180°, the matrix of the four ports is H = , in which an 1 −1 additional phase shift of 180° is present on the coupling arm. If the input LO signal is E loc , echo signal is E in , and the total phase shift θ of the coupler is not considered, then the output signals at the two output ports are √1 ejθ 2

⎧ 1 ⎪ ⎪ ⎨ E 1 = √ (E in + E loc ) 2 1 ⎪ ⎪ ⎩ E 2 = √ (E in − E loc ) 2

(3.11)

where E in = Ain cos[ωin t +φ(t)] (φ(t) is the phase shift generated during light signal transmission. Under different optical distances and different optical transmission conditions, φ(t) is a random variable); E loc = Aloc cos ωloc t. The two signals are separately input in a PIN diode corresponding to the balanced detector. R is the responsivity. Due to the square relation, the generated current is {

I1 = R · E 12 I2 = R · E 22

(3.12)

The following is obtained after substituting E in and E loc : I1 =

/ 1 R{Pin + Ploc + 2 Pin Ploc cos(γ ) cos[(ωin − ωloc )t + φ(t)]} 2

(3.13)

I2 =

/ 1 R{Pin + Ploc − 2 Pin Ploc cos(γ ) cos[(ωin − ωloc )t + φ(t)]} 2

(3.14)

Because the output of the balanced detector is the current difference of two PIN diodes, the output current is / Iout = 2R Pin Ploc cos(γ ) cos[(ωin − ωloc )t + φ(t)]

(3.15)

where cos(γ ) is related to the polarization directions of two optical signals and the line width. The narrower the line width is and the smaller the difference in the polarization direction, the larger the value of cos(γ ), which means a higher degree of coherence of two optical signals and stronger intensity of output signals of the balanced detector. For most practical fiber couplers, compared with the straight [arm, ]there is an 11 , then additional phase shift of 90° on the two coupled arms, H = √12 e j θ j j I1 =

/ 1 R{Pin + Ploc + 2 Pin Ploc cos(γ ) cos[(ωin − ωloc )t − π/2 + φ(t)]} (3.16) 2

114

I2 =

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

/ 1 R{Pin + Ploc + 2 Pin Ploc cos(γ ) cos[(ωin − ωloc )t + π/2 + φ(t)]} (3.17) 2

So, the output current is / Iout = 2R Pin Ploc cos(γ ) sin[(ωin − ωloc )t + φ(t)]

(3.18)

(3) Transmission and receiving system Due to the weak backscattering of the atmosphere, laser echoes are generally received using a large-aperture telescope. A transceiver optical system is used, with the core of a large-aperture Newtonian telescope. A plane mirror with the same size of, and parallel to, the secondary mirror is installed on the back of the secondary mirror of the Newtonian telescope. After passing through the optoisolator and beam expander, the outgoing laser from the fiber amplifier is incident on the plane mirror with an angle of 45°, so that the emitted laser is horizontally outgoing to the 2D scanning mirror. The Newtonian telescope is coupled into the single-mode fiber to fulfill the subsequent coherent detection. During atmospheric transmission of the polarized laser, the polarization state is changed due to the collision and scattering of photons by atmospheric particles [8]. Polarization diversity technique is to decompose mixed optical signals into two mutually orthogonal polarized components and then process them separately; finally, the sum of squares of the two intermediate-frequency (IF) signals is calculated to eliminate influences of random changes in the polarization state of signal light on the receiving sensitivity. The balanced detector can effectively inhibit common-mode interference in the system and improve the SNR [9]. The schematic diagram for polarization diversity reception of balanced mixing is illustrated in Fig. 3.23. LO light and signal light are separately expressed as E L (t) = E 10 cos(ωL t + ϕL ) E S (t) = E 20 cos(ωS t + ϕS )

Fig. 3.23 Schematic diagram for polarization diversity reception

(3.19)

3.1 Laser Detection on Atmospheric CO2 Disturbance

115

The horizontal and vertical polarized photoelectric fields are expressed below after processing using the 3 dB polarization-maintaining coupler (3 dB PMC) and polarization beam splitting [8]: ⎧ √ ⎪ E 1H (t) = ejϕ (γ E S (t)ejαH + χ E L (t)ej(δH +π/2) )/ 2 ⎪ ⎪ ⎪ √ ⎪ ⎨ E 2H (t) = ejϕ (γ E S (t)ej(αH +π/2) + χ E L (t)ejδH )/ 2 / / √ ⎪ E 1H (t) = ejϕ ( 1 − γ 2 E S (t)ejαV + 1 − χ 2 E L (t)ej(δV +π/2) )/ 2 ⎪ ⎪ ⎪ / / √ ⎪ ⎩ E 2H (t) = ejϕ ( 1 − γ 2 E S (t)ej(αV +π/2) + 1 − χ 2 E L (t)ejδV )/ 2

(3.20)

where ϕ is the phase of the reflection field; αH , αV , δH , and δV separately denote phase changes of signal light and LO light in the horizontal and vertical directions after polarization beam splitting; γ is the beam splitting ratio on the polarization beam splitter due to random changes in the polarization state of signal light; χ is the amplitude splitting ratio of the LO light after passing through the polarization beam splitter and it is χ 2 = 0.5 in the ideal case. The output signals of the two balanced detectors are {

/ IH (t) = 2ργ χ PL PS cos(2πΔ f t + ϕL − ϕS + δH − δH ) / / / IV (t) = 2ρ 1 − γ 2 1 − χ 2 PL PS cos(2πΔ f t + ϕL − ϕS + δV − δV ) (3.21)

The power of LO light after polarization beam splitting is monitored, thus obtaining χ 2 PL . In this way, the corrected formula for polarization diversity irrelevant to the LO light is obtained: I02 (t) = IH2 (t)/(χ 2 PL ) + IV2 (t)/[(1 − χ 2 )PL ] = 4ρ 2 PS cos2 (2πΔ f t + ϕL − ϕS + δV − δV )

(3.22)

This reveals that there is no information of LO light in Eq. (3.22), which is also irrelevant to the polarization beam splitting ratio γ of signal light. This actually solves the problem of reduced coherent efficiency due to changes in the angle between polarization states of echo signal and LO light and improves the coherent efficiency and coherent stability. (4) Other devices The all-fiber configuration is adopted in the experimental system, and the polarization-maintaining fiber, polarization beam splitter, polarization coupler, and double-balanced detector are widely used. In addition, the energy of emitted laser needs to be monitored at the transmitter of the laser transceiver, so as to normalize the energy of light echoes. At the outgoing end of the light amplifier, an optoisolator still needs to be connected to reduce influences of the high-energy laser reflected by ends of the all-fiber laser on working performance of the laser.

116

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

3.2 Detection of the Water–Vapor Concentration Water vapor, as a very important greenhouse gas, plays a critical role in many physical and chemical processes of the atmosphere. Moreover, water vapor is the only atmospheric composition that has phase changes. During phase changes, water vapor constantly releases or absorbs heat, which exerts great influences on the ground and air temperature. Therefore, persistent acquisition of distribution data of water–vapor concentrations with high precision and high spatial and temporal resolutions in the convective boundary layer is of important significance for studying the water cycle and the profile of latent heat flux of the atmosphere [10]. However, existing measurement methods all have some limitations. For example, ground-based detection towers can only obtain data at fixed ground points; although they can obtain water–vapor distribution data within a certain altitude range, sounding balloons cannot realize continuous observation due to limitations of multiple factors including the cost and climate; the spatial resolution of passive IR and microwave detectors cannot reach the requirement; ground-based GPS has large errors in detection of the lower atmosphere. The Raman lidar for atmospheric water–vapor measurement enables an advanced detection method, while its echo signals are so weak that the measurement precision under conditions of strong daytime background light will decrease significantly and effective data cannot be attained. DIAL is an advanced technology for active detection of various trace gas species developed since the 1970s. By two lasers with approximate wavelengths for simultaneous detection, the interfering variables can be eliminated through use of the difference method and the concentration distribution of the detected gas can be inverted. Such system can be self-calibrated and is not interfered by external factors. In addition, the echo signals consist of those of Mie scattering and Rayleigh scattering, which are stronger than Raman-scattered echo signals. Combined with certain noise reduction technologies, high detection precision can also be reached in the daytime. The US, France, and Germany all have established operable ground-based and airborne systems for water–vapor detection on the basis of DIAL. However, research on pulsed light water–vapor detection systems still remains blank in China. This section introduces a ground-based DIAL system scheme for water–vapor detection, which is used to measure the distribution of the water–vapor concentration at the bottom of the troposphere [11].

3.2.1 Detection Principle of the Water–Vapor Concentration According to the basic lidar equation, the energy of backscattered signals in the atmosphere can be expressed as

3.2 Detection of the Water–Vapor Concentration

117

⎡ ⎤ {R cτL A P(λ, R) = PL η(λ, R)β(λ, R) × exp⎣−2 α(λ, r )dr ⎦ 2 R2

(3.23)

0

where P(λ, R) is the echo power at the range of R; λ is the wavelength of emitted light; R is the receiving range; PL is the power of emitted light; c is the velocity of light; τ L is the laser pulse width; A is the effective area of the receiving telescope; η(λ, R) is systematic optical efficiency; β(λ, R) and α(λ, r ) separately denote the total backscattering coefficient and total extinction coefficient at the range of R. Because λon and λoff differ slightly, it can be regarded that laser beams of two wavelengths show basically consistent transmission characteristics in the atmosphere, they are affected to the same extent by factors including other gases and aerosols, and they have same systematic optical efficiency. Therefore, η(λon , R) ≈ η(λoff , R), β(λon , R) ≈ β(λoff , R), and α(λon , r) − α(λoff , r) ≈ ρ(H2 O, r)Δσ . It can be deduced from Eq. (3.24) that ρ(H2 O, R) =

[ ] P(λon , R)P(λoff , R + ΔR) 1 ln 2ΔRΔσ P(λon , R + ΔR)P(λoff , R)

(3.24)

where ρ(H2 O, R) is the average water–vapor concentration in the range from R to R + ΔR; ΔR is the range resolution; Δσ is the differential-absorption cross section, that is, the difference in water–vapor absorption cross sections of ON-line and OFF-line wavelengths. When inverting the water–vapor concentration, values of various parameters in the right-hand side of the equation need to be known. The echo energy P can be measured. The range resolution ΔR is an inversion parameter selected according to different situations. The differential-absorption cross section Δσ = σon − σoff can be attained by looking up the HITRAN database. However, due to influences of the frequency drift, line width, and spectral purity of the emitted laser, the actual absorption cross section (particularly σ on ) generally has certain deviation with values in the database, which directly incurs an error to the inversion results. To eliminate influences of such error, a real-time measurement device for the differential-absorption cross section based on a multi-channel gas cell is added in the system. The measurement principle of the device is introduced below. The light absorption of atmospheric molecules follows the Beer-Lambert law, so the following can be obtained: I2 (λ) = k × I1 (λ) exp{[−σ (λ) − εR (λ) − εM (λ)]n L}

(3.25)

where I1 (λ) and I2 (λ) separately represent the energy detected using high-speed detectors PD1 and PD2; k is the proportional constant between two channels, which comprehensively considers influences of the beam splitting ratios of the two channels, light path loss and difference in detector gain λ is the wavelength of incident light; σ (λ) is the absorption cross section corresponding to the wavelength (cm2 /molecule); εR (λ) and εM (λ) separately denote the Rayleigh scattering coefficient and Mie scattering coefficient; n is the number density of gas molecules absorbed per unit volume

118

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

Fig. 3.24 Inversion process of the water–vapor concentration

(molecules/cm3 ); L is the length of light-absorbing gas in the multi-channel gas cell that the light passes through. Likewise, due to the small difference between λon and λoff , the discrepancy in other factors excluding the different absorption cross sections can be ignored. The following is deduced according to Eq. (3.25): [ ] I2 (λoff ) I1 (λon ) 1 ln Δσ ≈ nL I1 (λoff ) I2 (λon )

(3.26)

The differential-absorption cross section Δσ can be calculated through the above equation and then calibrated according to the measurements. The whole inversion process is shown in Fig. 3.24.

3.2.2 Design of the DIAL Detection System A ground-based DIAL system for water–vapor detection is introduced here, and the block diagram for its constitution is shown in Fig. 3.25.

3.2 Detection of the Water–Vapor Concentration

119

Fig. 3.25 Block diagram for the constitution of the ground-based DIAL system for atmospheric water–vapor detection

3.2.2.1

Laser Emission

The emission part of the system is mainly composed of a Nd:YAG laser, seed lasers, and an optical parametric oscillator (OPO). The injection-seeded Nd:YAG laser outputs pulsed light with a narrow line width of 1064 nm at a repeated frequency of 10 Hz. After passing through the frequency doubling crystal, about 50% of energy is converted into pulsed light of 532 nm for pumping OPO. Two distributed feedback (DFB) lasers are adopted as the seed lasers, with their continuous light wavelengths separately located at and outside the absorption peak of water vapor, which are separately called the ON-line wavelength λon and OFF-line wavelength λoff . To achieve the alternate switching of output wavelengths of the OPO between λon and λoff , the two seed light beams are injected into the OPO after passing through a 2 × 1 photoswitch. A single-mode polarization-maintaining fiber and a half-wave plate are needed to ensure the alignment of polarization between pumping light and seed light. As displayed in Fig. 3.25, the OPO is composed of an annular cavity formed by four mirrors and nonlinear crystal potassium titanyl phosphate (KTP). PZT is installed on one of the mirrors to control the length of the annular cavity, so that the length matches with the wavelength of injected seed light. In the lower atmosphere, the absorption peak of water vapor has a width generally of 10 pm; while at the top of the troposphere, the width reduces to about 5 pm due to reduction of pressure and temperature. To ensure the high enough measurement precision, high requirements are set for the line width and frequency stability of ONline wavelength. Therefore, the Nd:YAG laser is used as the pumping source with the line width of about 150 MHz. Active frequency stabilization is adopted for the ON-line wavelength seed laser to stabilize the wavelength on the absorption peak of water vapor by taking the low-pressure all-fiber gas cell containing water vapor as

120

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

Fig. 3.26 Absorption lines of water vapor

the frequency reference. Passive frequency stabilization is adopted to the OFF-line wavelength seed laser due to low requirements for the wavelength stability. By using the seed injection technology, the wavelength of outgoing pulsed light from the OPO remains consistent with the seed light. The absorption lines for water vapor near 935 nm at the altitudes of 0 and 5 km in a mid-latitude region in summer are obtained from the HITRAN database, as shown in Fig. 3.26. Four absorption peaks λon1 = 935.450 nm, λon2 = 935.561 nm, λon3 = 935.776 nm, and λon4 = 935.906 nm are selected as the ON-line wavelengths, and λoff = 935.412 nm in close vicinity is selected as the OFF-line wavelength. The signal light output by the OPO hits the reflector after passing through the beam expander. The emitted light and the receiving telescope are aligned through 2D adjustment of the reflector.

3.2.2.2

Receiving Optics and Data Acquisition

A Cassegrain telescope with the aperture of 305 mm and focal length of 3048 mm is used to receive the backscattered signals. To allow working under strong daytime background light, a filter circuit composed of a collimating mirror, a narrow-band filter, and a convergent mirror is added in the rear end of the telescope. An APD is adopted as the photoelectric detector of echoes. The electrical signals of echoes are uploaded to the host computer after being digitized using a digital acquisition card.

3.2.2.3

Measurement of Differential-Absorption Cross Sections

To compensate for the error incurred by the frequency drift, line width, and spectral purity of the emitted laser, a real-time measurement system for differential-absorption cross sections is added. A small part of the emitted light is coupled into the fiber

3.2 Detection of the Water–Vapor Concentration

121

through a fiber collimator. Then, a polarization-maintaining fiber splitter is adopted to split the light into two beams. One beam is received by the high-speed photodiode PD1 detection circuit after passing through the 20-m-long multi-channel gas cell containing water vapor; the other beam is received directly by PD2 to serve as the reference. Finally, electrical signals of the two detectors are digitized using a dualchannel high-speed digital acquisition card (1 GSPS/12 Bit) and then uploaded to the host computer.

3.2.2.4

Sequential Control and the Host Computer

In the whole system, the Q-switching signal output by the Nd:YAG laser is used as the synchronizing signal. By using the sequential control circuit in the system, the photoswitch, PZT locked by the cavity length of the OPO, and the data acquisition system are allowed to work sequentially. The host computer is programmed using LabView to receive data uploaded by the data acquisition system and to real-timely invert and display water–vapor concentration profiles.

3.2.3 Performance Analysis and Simulation of Detection of the Water–Vapor Concentration 3.2.3.1

Relationship Between Emission Parameters and Detection Performance

Differential-absorption cross sections are affected by the frequency shuffling, line width, and spectral purity of the emitted laser. The spectral purity of the system is higher than 99.5%, with a random error lower than 3% in the region below 5 km; above 5 km, the error reduces to 1–2% with the decrease of the optical depth of water vapor [12]. The line width of the emitted laser of the system is about 200 MHz, which has a random error lower than 1% at an altitude of 5 km according to the absorption spectra of water vapor obtained from the HITRAN database. The ONline wavelength is stabilized on the absorption peak of water vapor through active frequency stabilization, and the frequency shuffling is within 50 MHz, with a random error lower than 1% at the altitude of 5 km. If these errors are considered to be independent of each other, the total random error incurred by the emitted laser is lower than 3.3% in the lower atmosphere. By using the above real-time measurement device for differential-absorption cross sections, the error can basically be eliminated. In fact, the device can also real-timely monitor the effectiveness of data and remove some idler pulses (occasional mismatch with the cavity length of the OPO).

122

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

Fig. 3.27 Relationship between different laser outgoing angles and overlap factors

3.2.3.2

Relationship of Receiving and Collecting Parameters with Detection Performance

For a side-axis system, the proportion of the overlap area between the receiving FOV of a telescope and the light spot of the emitted laser is termed as the overlap factor. The factor is a function of the receiving range, laser divergence angle, receiving FOV angle of the telescope, and the distance between two axes (laser and telescope). The simulation results of the overlap factor of the system are shown in Fig. 3.27. Because the ON-line and OFF-line wavelength lasers emit light alternately, their outgoing angles have certain deviation although the light is emitted from the same OPO, which may incur the random error in the range with the overlap factor smaller than 1. According to analysis results in previous research [13], the directional stability of ± 50 μrad may cause a random error greater than 15% in the range of 200–500 m. The error in the data acquisition part is mainly composed of the quantification error of the digital acquisition card and the nonlinear estimation deviation of background noises. When the SNR of echo signals is high, the error is basically ignorable; if the SNR of echo signals is low, the error may exert large influences and it mainly includes the absolute error and random error. At present, the error cannot be accurately simulated and estimated and it remains to be further studied through experiments.

3.2.3.3

Relationship Between Atmospheric Parameters and Detection Performance

The absorption cross section of water vapor is affected by pressure and temperature, so the uncertainty of temperature and pressure of the atmosphere at different altitudes may incur errors to the inversion. Many detailed theoretical analyses and experiments have been conducted. In the high-pressure region at a low altitude, the collision broadening induced by the pressure is dominant, and it is generally considered that the

3.2 Detection of the Water–Vapor Concentration

123

error of atmosphere density in the standard atmosphere model is lower than 3% [14]. The influence of pressure can be reduced to a level below 1% by measuring the ground pressure. In the low-pressure region, Doppler broadening caused by temperature begins to be dominant. If the temperature error is within ± 2 K, the error incurred by temperature at around 935 nm is also smaller than 1%. The pressure also may cause offset of the central wavelength of absorption lines, which, however, has a small influence. For example, the drift coefficient at 935.450 nm is − 0.1 pm/atm, which is basically negligible. The atmospheric backscattering mainly includes Rayleigh scattering by molecules and Mie scattering by aerosols. However, Rayleigh scattering by molecules broadens the echo spectra due to the Doppler effect, which incurs an error to the inversion. The aerosol concentration varies significantly and the proportion of Rayleigh-scattered signals in total scattered signals also differs in different weathers and at different altitudes. To improve the inversion precision, calibration according to different scattering ratios is needed. In the lower atmosphere where the aerosol concentration is high, the Rayleigh-Doppler effect only incurs a slight error that is generally lower than 1.5% in regions below 10 km. The backscattered signals also contain a small portion of Raman-scattered signals, which undergo certain frequency shift, different from the elastic scattered signals. Under conditions without a filter, the system error incurred by Raman scattering can reach 10% [15], so a narrow-band filter is needed in the nighttime, while a band width of 8 nm is needed in the nighttime compared with the filter of 1 nm in the daytime. The atmosphere mainly contains O2 , N2 , CO2 , H2 O, O3 , and other trace gas species. When selecting the laser wavelength, it needs to avoid absorption of undetected gases. It can be seen from data in the HITRAN database that at 935 nm, there is only an absorption peak of water vapor, so influences of absorption of other gas molecules can be excluded.

3.2.3.4

Relationship Between Echo Noises and Detection Performance

Noises in echo signals mainly include atmospheric background noise and detector noise. The atmospheric background radiation power in the nighttime is basically zero, while the background radiation power received by a telescope in the daytime is Pb = Sb π

(γ ) ΔλAT0 2

(3.27)

where Sb is the sky background radiation brightness, which is generally 3 × 10−3 W/ (m2 ·sr·nm) at 930 nm; γ is the receiving FOV angle of the telescope; Δλ is the band width of the filter; A is the effective area of the telescope; T0 is the optical efficiency of the receiving system. Noises of an APD detector mainly comprise the quantum noise, dark current noise, multiplication noise, and thermal noise of load resistance. If various noises

124

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

are regarded as independent of each other, the SNR of the voltage output by the detector can be expressed as / SNR =

[Pλ (z)R ∗ (λ)]2 M 2 2q(Pλ (z)R ∗ (λ) + Pb R ∗ (λ) + ID )M 2 F(M)Δ f + 2q IL Δ f +

4kb T Δ f RL

(3.28) where Pλ (z) is the power of scattered echo signals at the range of z; R ∗ (λ) is the responsivity of the detector at the wavelength of λ (M = 1); M is the multiplication factor of the APD;F(M) is the excess noise factor; Δf is the band width of the detector circuit; ID is the primary dark current of the APD; IL is the surface dark current of the APD; RL is the load resistance; T is the ambient temperature; q = 1.6 × 10−19 C; kb = 1.38 × 10−23 J/K. According to Eq. (3.28), the SNR of echoes is related to the multiplication factor M. Selecting an appropriate value of M can improve the SNR. Figure 3.28 illustrates the relationship curves between multiplication factor M and the SNR under different conditions. According to deduction results of Eq. (3.28), the correlations of various variables in the equation are ignored (In fact, echoes of ON-line and OFF-line wavelengths are not completely independent, so are echoes at ranges R and R + ΔR. However, it is difficult to accurately estimate their correlation, and the former and the latter are offset. Therefore, their correlations are ignored). Then the relative measurement error of the water–vapor concentration can be obtained as ⎛

⎞1/2

Σ 1 ⎜ 1 ⎟ σ [ρ(H2 O, R)] ⎜ ⎟ = ⎝ ρ(H2 O, R) 2Δτd i=1,2 SNRi,2 j ⎠ j=1,2

Fig. 3.28 Relationship between the multiplication factor M and the SNR of echoes (5 km)

(3.29)

3.2 Detection of the Water–Vapor Concentration

125

where Δτd = ρ(H2 O, R)ΔRΔσ is the differential optical depth of a scattering element with the thickness of ΔR, which is related to the range resolution, water– vapor concentration, and differential-absorption cross section; SNRi, j is the SNR of voltage of corresponding echo signals (i = 1, 2 separately represents the ON-line and OFF-line wavelengths; j = 1, 2 separately indicates the echo distances of R and R + ΔR).

3.2.3.5

Relationship Between the ON-Line Wavelength and Detection Performance

When selecting the ON-line wavelength, there is a contradiction: strong absorption lines can bring about high detection sensitivity and range resolution while at the same time, they also enlarge the echo attenuation, reduce the SNR of far-field echoes, and on the contrary increase the measurement error. The water–vapor concentration in the atmosphere is closely related to the region, season, and weather conditions, and it also affects selection of the ON-line wavelength. At a low concentration, a strong absorption peak needs to be used; while at a high concentration, a weak absorption peak should be adopted. Therefore, detailed simulation analysis is needed by combining with water–vapor concentrations of Shanghai in summer and winter in a bid to select the appropriate detection wavelength. Generally, the single-path differential optical depth is defined as {R τd =

Δσ N (r )dr

(3.30)

0

where R is the expected largest detection range; N (r ) is the gas concentration at a range of r ; Δσ is the differential-absorption cross section. The appropriate differential-absorption optical depth is in the range of 0.3 < τd < 1.5. The background radiation noise is low in the nighttime, so the optimal value of τd is 1.1; the background radiation noise is large in the daytime, so τd is valued to be 0.55 as the optimal value. According to absorption lines of water vapor in Fig. 3.26, the differential optical depths of various wavelength combinations in Shanghai in sunny days in summer and winter were numerically analyzed using the US Standard Atmosphere Model, as displayed in Fig. 3.29. By increasing ΔR (reducing range resolution) and pulse cumulative time Δt (reducing time resolution), the relative error can be reduced. Their relationship is shown as follows: σ [ρ(H2 O, R)] ∝ (Δt)−1/2 (ΔR)−3/2 ρ(H2 O, R)

(3.31)

With the increasing detection height, the water–vapor concentration decreases correspondingly. When Δτd remains unchanged, the range resolution ΔR decreases.

126

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

Fig. 3.29 Differential optical depths at various wavelengths in Shanghai in different seasons

By setting the pulse cumulative time Δt and differential optical depth Δτd of scattering elements separately to be 5 min and τd /20, relative errors of the water–vapor concentration detection at different altitudes under conditions of different detection wavelengths can be obtained by combining with analysis of the system measurement error in the above section. Detailed results are shown in Fig. 3.40. As can be seen from simulation results in Figs. 3.29 and 3.30, the differential optical depths of λon1 and λon4 are separately 0.36 and 1.28 at the altitude of 5 km in summer. The relative error of λon4 in nighttime detection is obviously smaller than that of λon1 ; in the daytime, as the altitude rises, the SNR of echoes becomes increasingly lower, and the relative error of λon4 gradually exceeds that of λon1 . In winter, the differential optical depths of λon3 and λon4 at the altitude of 5 km are 0.97 and 0.32, respectively. In the nighttime, the relative error of λon3 is obviously smaller than that of λon4 ; in the daytime, the relative error of λon3 is also lower than that of λon4 , while the former tends to exceed the latter with the rising altitude, which well conforms to the conclusion in previous research. In summary, three weak absorption peaks λon1 , λon3 , and λon4 near 935 nm are applicable to the ground-based DIAL system for water–vapor detection in Shanghai. Different ON-line wavelengths can be selected according to different seasons and weather conditions to achieve the optimal detection effect, which enables a measurement error of the water–vapor concentration not higher than 18%.

3.3 Analysis of Detection Performance for Disturbances of Atmospheric …

127

Fig. 3.30 Simulation results of relative errors of water–vapor concentration detection in Shanghai at different altitudes in different seasons

3.3 Analysis of Detection Performance for Disturbances of Atmospheric Component 3.3.1 Empirical Mode Decomposition and Data Preprocessing Performance The working principle of lidars is illustrated in Fig. 3.13. When a laser beam is transmitted in the atmosphere, light scattering occurs when the laser encounters scatterers (atmospheric molecules, aerosols, and clouds). Therein, the backscattered light returns to the lidar to be received by the receiving system. The needed atmospheric physical quantities can be obtained by inverting the received signals. For example, a DIAL system can detect the gas concentration in the atmosphere, and a Doppler lidar can invert the atmospheric wind velocity. Because of the weak backscattered signals of laser beams in the atmosphere, photon counting is generally adopted. The SNR of echo signals of lidars in the atmosphere √ is low, so the echo signals have to be processed. The SNR can be improved by m times after m times of cumulative averaging of signals. For a lidar at the repetition frequency of 10 Hz, 1000 times of cumulative averaging takes 100 s. Obviously, signal averaging cannot meet the need of some meteorological services and real-time detection. When it is used to analyze lidar echo signals at different ranges, wavelet finds it difficult to determine the wavelet threshold and type and it lacks of adaptivity [4]. Considering this, empirical mode decomposition (EMD) was adopted to process echo signals. EMD is a novel processing method first proposed by Huang in 1998 for analyzing nonlinear and non-stationary signals. EMD can decompose the energy of a signal according to fluctuations at various intrinsic scales in the time domain, thus obtaining a series of intrinsic mode functions (IMFs). The resulting IMFs of different scales obtained by decomposition correspond to different frequency components. EMD can effectively extract the trend of a data series and remove high-frequency noises in the

128

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

data series, so it can be used to real-timely process echo signals and improve the SNR. EMD is also adaptive. Compared with direct detection, coherent detection of laser echoes has a higher SNR. While to reach higher precision, the extracted echo signals still need to be denoised [16].

3.3.1.1

Simulation of Lidar Echo Signals in the Atmosphere

When using photon counting, the echo equation of lidars is expressed by ⎛ N = N0 ηβ(λ, R)ΔR(A/R 2 ) exp⎝−2

{R

⎞ α(λ, z)dz ⎠

(3.32)

0

where N0 is the photon number contained in a single laser pulse; N is the photon number received by the detector; λ is laser wavelength; η is the system receiving efficiency (including quantum efficiency of the detector); R is the detection range; A is the effective area of the telescope; β is the atmospheric backscattering coefficient (including the backscattering coefficients βm and βa separately of atmospheric molecules and aerosols); α is the atmospheric extinction coefficient (including the extinction coefficients αm and αa separately of atmospheric molecules and aerosols). Here, the echo signals of the lidar at 532 nm for detecting the atmospheric wind velocity were simulated and the parameters of the lidar system used are shown in Table 3.3. The extinction modes of atmospheric molecules and aerosols adopted in the numerical simulation are expressed by Eqs. (3.33) and (3.34), and the simulated lidar echo signals are displayed in Figs. 3.31 and 3.32. {

βm (z) = 1.54 × 10−3 exp(−z/7) αm (z) = βm (z) × 8π/3

{

(km−1 )

(3.33)

] [ βa (z) = 2.47 × 10−3 exp(−z/2) + 5.13 × 10−6 exp −(z − 20)2 (km−1 ) αa (z) = βa (z) × 50 (3.34)

3.3.1.2

EMD Processing of Lidar Echo Signals in the Atmosphere

According to the EMD process, the lidar echo signals in the atmosphere were processed, and the processing results are displayed in Fig. 3.33. IMF1–IMF4 are decomposed IMFs at different scales, and H is the trend term. By using the Monte Carlo method, the echo signals are subjected to 100 times of simulation and cumulative averaging and then compared with the EMD processing results of any simulated signal, as shown in Fig. 3.34. Figure 3.35 illustrates the

3.3 Analysis of Detection Performance for Disturbances of Atmospheric … Table 3.3 Parameters of the lidar system

Fig. 3.31 Lidar echo signals in the atmosphere

Fig. 3.32 Mono-pulse lidar echo signals at the altitude of 5–6 km

129

Parameters

Values

Parameters

Values

Pulse power

50 mJ

Wavelength

532 nm

Pulse width

100 ns

Telescope aperture

200 mm

Receiving FOV

0.5 mrad

Optical utilization rate

0.3

Quantum efficiency

0.4

Band width of the filter 1.5 nm

Dark count in the APD

500/s

Background radiation brightness

0.14 W/ m2 sr nm

130

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

Fig. 3.33 EMD processing results of lidar echoes in the atmosphere

deviations of results of 100 times of signal averaging and EMD processing results from the original signals. Figures 3.36 and 3.37 show the linear regression results of the IMF reconstructed signal and the mono-pulse signal with 100 times of signal averaging. The IMF reconstructed signal is approximate to the results of 100 times of signal averaging. The correlation coefficient and standard deviation are calculated to be 0.99 and 2.8, respectively. However, the correlation coefficient and standard deviation of the mono-pulse signal with the results of 100 times of signal averaging are 0.86 and 13.4, respectively. Fig. 3.34 Results of 100 times of signal averaging and EMD processing results

3.3 Analysis of Detection Performance for Disturbances of Atmospheric … Fig. 3.35 Deviation of the processing results from the original signal

Fig. 3.36 Average linear regression of the IMF reconstructed signal with 100 times of signal averaging

Fig. 3.37 Average linear regression of the mono-pulse signal with 100 times of signal averaging

131

132

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

3.3.2 Inversion Precision of Gas Concentrations 3.3.2.1

DIAL Precision in Coherent Detection of the Atmospheric CO2 Concentration

When using photon counting, the SNR of a single pulse is [4] SNR = √

Nc Nc + (Nb + Nd )

(3.35)

where Nc , Nb , and Nd separately denote the photon numbers of echo signals, background noises, and dark current noises of detectors. When the detection range is long, it is regarded that n off,1 = n off,2 . It generally requires that the absorption cross section of the light source λoff is smaller than that of the light source λon , that is, σon ≫ σoff , which implies that n on,1 = n off,1 exp(−2τ1 ) {R and n on,2 = n off,2 exp[−2(τ1 + Δτ1 )]. Therein, τ1 = 0 1 Δσ N (r )dr , which is called the differential-absorption optical depth of CO2 . Assuming that the numbers of cumulative pulses of the light sources λon and λoff are M11 and M12 , then the relative error of detection after pulse cumulative averaging is [17] 1 Δn = n 2Δτ1

(

−2 −2 −2 −2 Son,1 + Son,2 + Soff,2 Soff,1 + M11 M12

τ1 and X = Assuming that K = Δτ 1 Eq. (3.36) is rewritten as follows:

n B +n D , n i, j

)1/2 (3.36)

according to the above analysis,

K 1 Δn = n 2Soff,2 τ1 (1 + X )1/2 { }1/2 [ ( )] [ ( )] exp(2τ1 ) 1 + exp 2τK1 + X exp(4τ1 ) 1 + exp 4τK1 2(1 + X ) + × M11 M12 (3.37) Similar analysis is performed for coherent detection. Then the relative error of coherent detection is }1/2 { [ ( )] K 1 exp(4τ1 ) 1 + exp 4τK1 Δn 2 = + n 2Soff,2 τ M11 M12

(3.38)

Generally, coherent detection has a high SNR and high detection sensitivity, so the coherent detection precision is mainly analyzed. The precent variation of relative error is defined as [( Δn ) − ( Δn ) ]/( Δn ) × n n min n min 100%. Assuming that K is separately valued to be 3, 6, 10, 20, 50, 100, and 1000,

3.3 Analysis of Detection Performance for Disturbances of Atmospheric …

133

M11 = M12 = 1800, and SNR of echoes is SNR = 10, then changes in the precent variation of relative error with the differential-absorption optical depth τ1 of atmospheric CO2 are attained, as displayed in Fig. 3.38. It can be seen from Fig. 3.38 that as K increases, the precent variation of relative error tends to be stable with variation of τ1 . The change in relative error is minimum (0) when τ1 = 0.55, indicating that the detection precision is the highest under the condition. Supposing that K = 70 and M11 = M12 = 1800, the SNR of echoes is separately set to be 5, 10, 20, 50, and 100, thus obtaining change curves of the relative error with the differential-absorption optical depth τ1 of atmospheric CO2 , as shown in Fig. 3.39. With the increase in the SNR, the relative error reduces. The detection precision is highest when τ1 = 0.55. Fig. 3.38 Change curves of the precent variation of relative error with τ1

Fig. 3.39 Change curves of the relative error with τ

134

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

Fig. 3.40 Change curves of the detection error with the altitude

CO2 has a strong absorption peak at about 1.5 μm [6]. Here, detection sensitivity of the lidar system at 1.5 μm for atmospheric CO2 detection was numerically simulated. A balanced detector was used in the coherent detection, that is, two PIN photodiodes with the same performance. When the power of LO light is larger than that of background light, the noises of coherent detection mainly come from the shot noise of LO light. Assuming that the responsivity of the detector is χ , the band width of the detector is B, LO power is Pl , echo power is Ps , background radiation power is Pb , working temperature is T , and resistance is R, then the SNR of a single pulse in coherent detection is √ (2χ Ps Pl )2 (3.39) SNR = 2B[χ e(Ps + Pl + Pb ) + 2kT /R] where e is the electron charge; k is the Boltzmann constant. Typical parameters of the lidar system were selected, as listed in Table 3.4. The CO2 content in the common atmosphere is 330 × 10–6 . Change curves of the relative error with the altitude are obtained by simulating the fiber lidar system at 1.5 μm, as shown in Fig. 3.40. The detection precision at 1 km is 60 × 10–6 . Analysis of the coherent detection precision concludes that when the differentialabsorption optical depth of atmospheric CO2 is τ1 = 0.55, the coherent detection system shows the minimum precent variation of relative error, so the detection precision is the highest in the case of τ1 = 0.55. Through numerical simulation of the fiber lidar system at 1.5 μm for coherent detection of CO2 , the following conclusion is obtained: as the detection height rises, the detection precision of the system gradually reduces, and the system can detect changes of atmospheric CO2 by 60 × 10–6 within the altitude range of 1 km. As the laser power grows, cumulative time prolongs, and the number of cumulative pulses increases, the precision can be further improved.

3.3 Analysis of Detection Performance for Disturbances of Atmospheric … Table 3.4 Parameters of the fiber lidar system

3.3.2.2

135

Parameters

Values

Parameters

Values

EDFA outgoing power

50 W

LO power

2.5 mW

Pulse width

100 ns

Repetition frequency

20 kHz

Telescope aperture

350 mm

Optical efficiency

0.5

FOV

0.5 mrad

Detection responsivity

0.95 A/ W

Band width of the filter

1 nm

Band width of the detector

50 MHz

Background spectral radiance

1 W/ m2 sr μm

Pulse cumulative time

10 min

Temperature

273 K

Resistance

50 Ω

Precision of the DIAL System Adopting an Optical Circulator

An all-fiber lidar, as a novel detection means, has been widely applied to fields such as 3D wind-field measurement, target velocity measurement, and gas concentration detection. In recent years, all-fiber DIAL systems have found extensive applications due to their advantages such as the simple structure, good system stability, and high detection sensitivity, and increasingly more research has been conducted on their system structure and application characteristics. Previous research mainly focuses on analysis of the overall detection precision of DIALs and processing of experimental data of the systems. However, few research is carried out on influences of the DIAL design on the detection precision. Previous research has shown that optical circulators, as simple and practical optical devices, are commonly applied as transmit-receive switches in optical transceiver antennas in all-fiber lidar systems due to their excellent unidirectional transmission capacity. However, if the lidar system is used for detection of urban gas pollution or CO2 in forests, the crosstalk in an optical circulator may interfere the laser echo signals due to the short detection range, thus seriously impairing the detection SNR and precision. The section analyzes the SNR and detection precision of the DIAL and discusses how the crosstalk of the optical circulator influences gas detection. (1) SNR of the detection system When using photon counting, the photon number of noises in Eq. (3.35) is [4] ⎧ ⎨

λ 2ΔR (πθ D)2 ηΔλ hc c 16 ⎩ Nd = QΔt Nb = L b

(3.40)

136

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

where L b , h, θ , and D separately denote the background radiation brightness, Planck constant, receiving FOV angle, and aperture of the receiving telescope; Δλ, c, Q, and Δt separately represent the band width of the filter, velocity of light, dark count, and pulse width. If the lidar works in the nighttime, the photon number of background radiation is negligible. In the mode of photon counting, the photon number of lidar echo signals √ in the atmosphere obeys Poisson’s distribution with the standard deviation of N . The photon number of noises is also regarded as following Poisson’s distribution. Taking CO2 detection as an example, in the atmospheric mode at 1572 nm, it is supposed that the CO2 concentration below 1000 m is distributed as Nw (z) = N0 exp(−z/7) with the altitude; meanwhile, the ground CO2 concentration is set to be N0 = 1.048 × 1016 cm−3 , and the absorption cross sections are separately σon = 6.36 × 10−23 cm2 and σoff = 4.56 × 10−24 cm2 . Combining with Eqs. (3.33) and (3.34), the following can be obtained [18] β(R) = 0.8398 × 10−3 exp(−R/2) + 1.74 × 10−6 exp[−(R − 20)2 /36] + 2 × 10−5 exp(−R/7) α(z) = 4.2 × 10−2 exp(−z/2) + 8.7 × 10−5 exp[−(z − 20)2 /36] + 1.67 × 10−4 exp(−z/7)

(3.41)

Because an optical circulator is used in the detection system, within the time corresponding to the pulse width τ0 at the beginning of laser emission, that is, when the detection location is within the range of cτ0 /2, crosstalk of the optical circulator is mixed with in echo signals received. Figure 3.41 shows that sampling points R1 and R2 are both within the range of cτ0 /2; Fig. 3.42 depicts that only sampling point R1 is within the range of cτ0 /2 while sampling point R2 is beyond the range of cτ0 /2. Because the crosstalk-induced interfering signal is superposed on laser echoes in the range of cτ0 /2, a crosstalk-induced interference term needs to be added in the noise term of Eq. (3.35). Then, calculation of the SNR turns to Fig. 3.41 Sampling points are both within the range of cτ0 /2

3.3 Analysis of Detection Performance for Disturbances of Atmospheric …

137

Fig. 3.42 Sampling points are within R1 < cτ0 /2 < R2

SNR = √

Nc Nc + (Nb + Nd ) + N ,

(3.42)

where N , = 10−6 (Np,on , Np,off ), which is because the interfering signal is induced by crosstalk in the optical circulator. According to the above description, when different sampling locations R1 and R2 as well as sampling ranges are selected, the simulation results are illustrated in Fig. 3.43. The dashed lines in the figure indicate the case without the optical circulator [corresponding to Eq. (3.35)]. Changes in the system SNR with the detection height from the top to the bottom correspond to change curves of the SNR with the sampling ranges ΔR of 50, 40, and 30 m. The solid lines indicate changes in the system noises with the detection height when using the optical circulator [corresponding to Eq. (3.42)]. From the top to the bottom, they separately correspond to change curves of the SNR with the sampling ranges ΔR of 50, 40, and 30 m. It can be seen from Fig. 3.43 that the longer the sampling range ΔR is, the higher the SNR. Compared with the SNR in the case of not using the optical circulator, the system SNR reduces abruptly in the range of cτ0 /2 + ΔR after using the optical circulator. This is because the crosstalk-induced interfering signal of the optical Fig. 3.43 Change curves of the SNR with the height

138

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

circulator is mixed with in laser echoes in the range. The reduction of SNR seriously impairs the detection precision of the system. When the detection range is beyond cτ0 /2 + ΔR, crosstalk of the optical circulator disappears and the SNR restores to the original value because the sampling points are not within the range of cτ0 /2. Under the condition, the problem that crosstalk of the optical circulator influences echoes will never happen any longer. The abscissa values of solid lines represent the detection height that can be affected by crosstalk-induced interference of the optical circulator under different sampling ranges ΔR. If replacing the PMT in Fig. 3.42 with a photoelectric detector, the system can be applied to IR detection. The similar method can be used for analysis, which reveals that the above problem pertaining to crosstalk will also occur. (2) Analysis of detection precision According to Eq. (3.35), the larger the differential-absorption cross section is, the higher the SNR of echoes and the lower the detection error of gas concentrations. Light sources λon and λoff are assumed to have a same number of cumulative pulses, M, and the background noise NB is ignored. Through cumulative averaging of pulses, the relative error is 1 ΔN −2 −2 −2 1/2 = (S −2 + Son,2 + Soff,1 + Soff,2 ) N 2MΔτ on,1

(3.43)

When R1 < R2 < cτ0 /2, crosstalk affects calculation of the SNR at locations of R1 and R2 , as displayed in Fig. 3.41. According to previous analysis of the SNR, the crosstalk N , of the optical circulator needs to be added in the noise terms of all SNRs Son,1 , Son,2 , Soff,1 , and Soff,2 . The SNR is calculated by substituting relevant parameters into Eq. (3.42). Then, the relative error can be calculated by substituting the SNR into Eq. (3.35). If R1 < cτ0 /2 < R2 , as displayed in Fig. 3.42, crosstalk only affects SNR calculation at location R1 , that is, the interfering term N , should be added when calculating Son,1 and Soff,1 [corresponding to Eq. (3.42)]; while it does not influence SNR calculation at location R2 [corresponding to Eq. (3.35)]. In the case that cτ0 /2 < R1 < R2 , crosstalk of the optical circulator does not affect SNR calculation of any longer. According to the above analysis, the relative error within the sampling range of 500 m can be calculated. The calculation results are shown in Fig. 3.44. In Fig. 3.44, the solid lines indicate the change trend of the relative error with the detection height in the case of not using the optical circulator; the dashed lines represent changes in the relative error with the detection height in different sampling ranges after using the optical circulator. It can be seen that the longer the sampling range is, the lower the relative error. Compared with the relative error under condition that not using the optical circulator, the relative error increases abruptly within the range of cτ0 /2 + ΔR after using the optical circulator. In addition, two obvious inflection points are observed at cτ0 /2 and cτ0 /2 + ΔR. This is because of different influences of crosstalk on the SNR at different sampling locations. Therefore, the

3.3 Analysis of Detection Performance for Disturbances of Atmospheric …

139

Fig. 3.44 Change curves of the relative error with the height

use of the optical circulator in the detector seriously impairs the detection precision for the lower atmosphere. Therefore, when designing systems and determining indices, influences of the introduction of crosstalk on the detection precision should be considered if the atmospheric concentration at a low altitude needs to be detected. Such influences may affect the detection of urban polluting gases and CO2 concentration in forests. The section only studies close-range gas detection. When a high-energy laser is used for long-range detection, the crosstalk produced by the next laser pulse being emitted will be mixed in the laser echo signal of the previous pulse returned at 7.5 km because laser pulses are emitted at a certain frequency (e.g., 20 kHz). As a result, the gas detection results are incorrect at the location near 7.5 km, thus causing a vague zone in intermittent detection. The space-based lidar and upper-atmosphere detection lidar both belong to long-distance detection lidars, so the above influence needs to be considered during system design, which is not intensively explored here. Additionally, the application of the optical circulator to the differential-absorption system was also analyzed and simulated. For a coherent Doppler lidar, because crosstalk-induced interference is far larger than the laser echo signal of targets, the coherent echo signal is masked by crosstalk-induced interference in the band width of interfering noises after coherence of crosstalk and echoes with LO signals at the same time if the Doppler shift is small. This also affects the index of the minimum velocity measurement precision. In the case of high requirements for velocity indices such as low-altitude wind-field detection, such design has the corresponding drawback. In addition, a long-pulse laser is generally selected to narrow the line width and increase the coherence efficiency in a bid to improve the velocity measurement precision of coherent detection, thus enlarging the minimum effective detection range. This, however, also increases the range of the vague zone in intermittent detection. If a short-pulse laser is used to reduce the minimum effective detection range, the band width of the coherent interfering signal is broadened, so the minimum measurement precision obtained through inversion of frequency shift will be reduced. Therefore,

140

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

the design of this part is contradictory and the detailed design should be considered comprehensively according to the practical design indices. For a coherent DIAL, crosstalk noises of the optical circulator are also mixed in the coherent laser echo signal. If directly using the signal as the echo intensity to invert gas concentrations, instead of eliminating the system fixed error in advance by signal processing, the detection SNR and precision will decrease significantly. Because system parameters are given, data collected in the range affected by interference can be preprocessed by subtracting the fixed intensity error incurred by coherence between crosstalk and LO signal from the echoes, followed by data inversion to reach the goal of reducing or eliminating crosstalk. The specific effect still remains to be experimentally verified. Optical circulators as commonly used optical devices are generally adopted as optical converters and transmit-receive switches. At present, optical circulators already have high optical isolation. However, under conditions of extremely weak laser echo signals such as atmospheric detection, the weak crosstalk-induced interference also heavily affects the detection precision. By studying the all-fiber DIAL using the optical circulator as the transmit-receive switch, it has been confirmed that the use of the optical circulator heavily impairs the detection SNR and precision when detecting gas concentrations in a close range combining with the simulation results. Analysis methods and conclusions in the section are of important guiding significance for system design of coherent lidars.

3.3.3 Influences of Atmospheric Attenuation and Turbulences on Detection Performance Optical heterodyne detection can comprehensively extract the amplitude, frequency, and phase of echo signals compared with direct detection technologies. It can obtain the velocity and acceleration of objects through frequency measurement and subnanometer resolution through refined phase measurement. Therefore, heterodyne detection has been widely applied and developed. However, current application of heterodyne detection has been mainly focused on detection of close targets and extraction of motion information. In long-range detection, heterodyne detection is mainly applied to research on free-space coherent optical communication and fiber communication. Heterodyne detection lidars are seldom used to atmospheric transmission mainly because the atmosphere not only causes attenuation of light wave amplitude and Doppler shift of frequency, but also leads to phase disturbances. Therefore, it is necessary to explore influences of the atmosphere on heterodyne detection lidars to further study measures for eliminating, weakening, or compensating for influences of atmospheric disturbances.

3.3 Analysis of Detection Performance for Disturbances of Atmospheric …

3.3.3.1

141

Heterodyne Efficiency

The basic constitution of a typical heterodyne detection system is shown in Fig. 3.45. From the perspective of complex amplitude of light, assuming that the signal and LO light beams share the same polarization and they both vertically illuminate the uniform optical surface of the detector, then the variation of complex amplitude of light at point (x, y) on the detection surface is U˜ S (x, y, t) = AS0 (x, y) exp[i(ωS t + φS )] U˜ L (x, y, t) = AL0 (x, y) exp[i(ωL t + φL )]

(3.44)

where AS0 (x, y) and AL0 (x, y) are the amplitudes of optical fields of signal light and LO light on the detector optical surface; ωS and ωL are angular frequencies of signal light and LO light; φS and φL are phase angles of signal light and LO light, respectively. Then, the total radiation field on the photoelectric detector is U (x, y, t) = US (x, y, t) + UL (x, y, t)

(3.45)

When detection elements on the detector surface uniformly respond to the incident light, namely, η(x, y) = η, the intrinsic impedance on the detector surface is Z0 . After DC removal and IF filtering, the IF current output by the whole detector is ¨ i IF (x, y) =

di IF (x, y, t)dS = D

2eη

˜ D

AS0 (x, y)AL0 (x, y) cos[ωIF t + (φS − φL )]dS Z 0 hv

(3.46) where ωIF = ωS − ωL is called the IF. Obviously, the output current of heterodyne detection is related to the amplitude, frequency, and phase of signal light. The heterodyne detection efficiency is defined as [19] Fig. 3.45 Basic constitution of a heterodyne detection system

142

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

|2 |˜ | | ∼∗ D UL (x, y, t)US (x, y, t)dS] ηh = ˜ { { 2π ∞ 2 2 0 US (x, y, t)dS D UL (x, y, t)dS · 0

(3.47)

The heterodyne detection efficiency characterizes the match degree between incident signal light and LO light and reflects the magnitude of the SNR of echoes.

3.3.3.2

Influences of Atmospheric Attenuation and Atmospheric Turbulences

When it is transmitted in the atmosphere, the laser will be absorbed and scattered by atmospheric molecules and aerosols, thus attenuating the energy of light beams. Generally, the spectral attenuation coefficient σ (λ) is used to describe influences of two independent physical processes (absorption and scattering) on the radiation intensity of transmitted light. In accordance with the Beer-Lambert law, the optical field represented by complex amplitude of the laser with emission intensity of I0 after transmission for a range of L is changed into U˜ S (x, y, t) = AS0 (x, y) exp[−σ (λ)L/2 + i(ωS t + φS )]

(3.48)

The atmospheric turbulence effect changes the optical distance of light waves in time and space during transmission through turbulences and causes the wavefront difference, thus affecting the transmission of light waves. Influences of the atmospheric turbulence effect on the transmitted light beams are mainly shown as degradation of coherence. Due to degradation of coherence, physical quantities including the light amplitude, frequency, phase, wave vector, and beam radius all have random changes, which are shown as angle-of-arrival (AOA) fluctuation, intensity fluctuation, beam expansion, beam shift, and image dithering. These effects may happen at the same time [20]. The additional phase delay due to transmission of light beams is ignored in the above deduction. That is, each point in the ranges of signal and LO light beams is considered to have the same phase delay. The influence of additional phase delay on the output IF current is only shown as addition of a fixed phase difference on the phase. When there are atmospheric turbulences, the signal light is affected by the turbulence, so that the phase delay of various points in the light spot is different. If not considering the atmospheric attenuation effect, the signal light under the condition is U˜ S (x, y, t) = AS0 (x, y) exp{i[ωS t + φS + kS (x, y)L S ]}

(3.49)

AOA fluctuation reflects the random fluctuation of the equiphase surface of light waves. The influence of such an effect on the signal light is shown by the phase term exp{i[kS (x, y)L S ]}. Beam shift characterizes the influence of large-scale turbulences on the light beam inclination. Beam expansion characterizes the influence

3.3 Analysis of Detection Performance for Disturbances of Atmospheric …

143

of small-scale turbulences on the spot radius and its influence on the signal light is shown by the center offset and range of (x, y). Light intensity fluctuation reflects the influence of atmospheric turbulences on the amplitude of transmitted light beams and its influence on the signal light is shown by AS0 (x, y). It is evident that the corresponding heterodyne efficiency can be attained as long as the phase, amplitude, and transmission direction of light beams after transmission through turbulences, as well as the spatial distribution of changes in the light spot size on the detector surface are known. The research reveals that when the transmitted light beam is spherical waves and the 2D spatial radius vector r is between inner and outer scales of turbulences, phase fluctuation can be expressed by the following equation [17] Dphase (r, L) = 1.903Cn2 Lk 2 r 5/3

3.3.3.3

(3.50)

Numerical Simulation

The most important point in numerical simulation of heterodyne efficiency is the waveform model of echo signals. At present, light wave models for simulation mainly include the spherical wave model, Gaussian model, and Airy-disk model. However, in general cases, signal light is received by a circular aperture and the light spot distribution on the detection surface follows Airy-disk distribution. So, changes in heterodyne efficiency were analyzed based on the Airy-disk model. Meanwhile, to simplify the analysis, the polar coordinates were used to represent the optical-field distribution, then U˜ S (x, y, t) = AS0 (r, θ ) exp[−σ (λ)L/2] exp[i (ωS t + φS )] exp[ikS (x, y)L S ] (3.51) (1) Influence of atmospheric attenuation Atmospheric attenuation does not change the equiphase surface of light waves, so the additional phase term in the above equation is not considered. Then, the Airy-disk model for signal light and LO light on the detector is ∼S (r, θ ) = J1 (X S ) exp(−σ (λ)L/2) U XS

(3.52)

∼L (r, θ ) = J1 (X L ) U XL

(3.53)

where X S = kS dr/ f, X S = kL dr/ f ; k S and k L separately represent the values of wave vectors of signal light and LO light; r , d, and f separately denote the detector radius, aperture radius, and focal length of lens; θ ≈ arcsin r/ f . The following is obtained according to the definition of heterodyne efficiency:

144

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

Fig. 3.46 Heterodyne detection efficiency near the wavelength of 1550 nm

|˜ | | D

ηh = ˜

J1 (X L ) J1 (X S ) XL XS

|2 | exp[−σ (λ)L/2]dA|

{ 2π { ∞ [ J1 X(XLL ) ]2 dA 0 0 { J1 X(XS S ) exp[−σ (λ)L/2]}2 d A |˜ |2 | | | D J1 X(XLL ) J1 X(XS S ) | = ˜ J (X ) { 2π { ∞ J1 (X S ) 1 L 2 2 0 [ XS ] d A D [ XL ] d A 0 D

(3.54)

Therefore, atmospheric attenuation alone does not affect the heterodyne efficiency. The SNR reduction of echoes is a result completely of the reduced energy of echo signals. Figure 3.46 displays heterodyne efficiency near the detection wavelength of 1550 nm. It can be seen from Fig. 3.46 that the heterodyne detection efficiency rises with enlargement of the detector radius (Fig. 3.46a), and reduces with the frequency difference between signal light and LO light, while such difference is unobvious (Fig. 3.46b). (2) Influence of phase fluctuation Phase fluctuation impairs the pointwise fixed phase difference on the light spot, causes inconsistent directions of current vectors of microelements on the detector, and then results in the reduced total IF current, finally leading to great reduction of the heterodyne detection efficiency. If the structure function of the atmospheric refractive index is 1.7×10−15 m−2/3 , it can be seen from Fig. 3.47 that the heterodyne detection efficiency of coherent light beams reduces significantly after transmission for different ranges in atmospheric turbulences. The transmission of coherent light beams in the atmosphere is under the joint influence of atmospheric attenuation and atmospheric turbulences. This leads to reduction and random variation of the amplitude of transmitted light beams, as well as random variation of phase, transmission direction of different points in light beams, and the beam waist. The amplitude reduction of light beams caused by atmospheric attenuation does not change the heterodyne detection efficiency while leads to variation of

References

145

Fig. 3.47 Influence of phase fluctuation on heterodyne detection efficiency

the detection SNR. Variation of atmospheric turbulences decreases the heterodyne detection efficiency and influences the SNR of echoes.

References 1. Koch GJ, Barnes BW, Petros M, et al. Coherent differential absorption lidar measurements of CO2 . Appl Opt. 2004;43(26):5092–9. 2. Wu XQ. CO2 test report of Nanjing on July 10, 2005. 3. Hong GL. Detection method and experiment of atmospheric CO2 lidar. Hefei: Anhui Institute of Precision Machinery, Chinese Academy of Sciences; 2005. 4. Spinhirne JD. Micro pulse lidar. IEEE Trans Geosci Remote Sens. 1993;31(1):48–55. 5. Liu H, Hu YH, Hong GL, et al. Continuous wave differential absorption lidar measurement of atmospheric CO2 . J Phys. 2014;63(10):104214–10220. 6. Sakaizawa D, Nagasawa C, Nagai T, et al. Development of a 1.6 μm differential absorption lidar with a quasi-phase-matching optical parametric oscillator and photon-counting detector for the vertical CO2 profile. Appl Opt. 2009;48(4):748–57. 7. Liu H, Chen T, Shu R, et al. Wavelength-locking-free 1.57 μm differential absorption lidar for CO2 sensing. Opt Express. 2014:22(22):27675–80. 8. Yang LH, Ke XZ, Ma DD. Research on depolarization of polarized laser in atmospheric transmission. Optoelectron Eng. 2008;35(11):62–7. 9. Dong X, Hu YH, Zhao NX, et al. Research on polarization diversity technology in differential absorption coherent lidar. Optoelectron Laser. 2015;26(3):541–7. 10. Kiemle C, Ehret G, Fix A, et al. Latent heat flux profiles from collocated airborne water vapor and wind lidars during IHOP_2002. J Atmos Ocean Technol. 2007;24(4):627–39. 11. Ge Y, Shu R, Hu YH, et al. Design and performance simulation of ground-based differential absorption lidar system for atmospheric water vapor detection. J Phys. 2014;63(20):199–206. 12. Poberaj G, Fix A, Assion A, et al. Airborne all-solid-state DIAL for water vapour measurements in the tropopause region: system description and assessment of accuracy. Appl Phys B. 2002;75(2–3):165–72. 13. Wulfmeyer V, Bösenberg J. Ground-based differential absorption lidar for water-vapor profiling: assessment of accuracy, resolution, and meteorological applications. Appl Opt. 1998;37(18):3825–44.

146

3 Laser Detection on Atmospheric Components Disturbed by Aerial …

14. Russell PB, Morley BM, Livingston JM, et al. Orbiting lidar simulations. 1: Aerosol and cloud measurements by an independent-wavelength technique. Appl Opt. 1982;21(9):1541–53. 15. Browell EV, Butler CF, Ismail S, et al. Airborne lidar observations in the wintertime Arctic stratosphere: ozone. Geophys Res Lett. 1990;17(4):325–8. 16. Tao XH, Hu YH, Cai XC. Accuracy analysis of differential absorption lidar detection of atmospheric CO2 . J Atmos Environ Opt. 2008;3(2):100–3. 17. Tao XH, Hu YH, Lei WH, et al. Empirical mode decomposition for lidar atmospheric echo processing. Laser Technol. 2008;32(6):590–3. 18. Tao XH, Hu YH, Cai XC, et al. Research on the performance of low altitude CO2 lidar system based on OPO laser detection. Laser J. 2008;29(1):78–9. 19. Wang EH, Hu YH, Li L, et al. Analysis of the impact of atmospheric external difference detection lidar. Infrared Laser Eng. 2011;40(10):1896–9. 20. Dong X, Hu YH, Zhao NX, et al. Research on the influence of atmospheric turbulence on the accuracy of coherent lidar CO2 detection. Optoelectron Laser. 2015;26(7):1314–21.

Chapter 4

Active Imaging Detection on Target Retroreflection Features

Retroreflection characteristic refers to the reflected light beam propagates along the direction of a light source and remains within a small solid angle. Such characteristic still remains even if the direction of incident light is changed in a large range. Retroreflectors are widely applied to fields calling for safe nighttime operation, such as runways, indication signs, and anti-collision signs on aircraft landing platforms and various motion platforms and cave depots of vehicles and vessels, as well as all kinds of traffic signs. Retroreflectors, attached on aircraft landing platforms, motion platforms and cave depots, and traffic signs, belong to typical target-attached attributes. By detecting the retroreflection characteristic, detection activities including detection, location, and identification of the above targets can be achieved. The chapter mainly discusses the principle of active imaging detection of retroreflection characteristic, quantification and realization of retroreflection characteristic, and detection algorithms of retroreflectors, and elaborates a retroreflector detection method.

4.1 Detection Principle of Retroreflection Features of Targets Retroreflection features are special optical property. To detect retroreflective attributes, such characteristic needs to be effectively captured using an imaging device at first, that is, quantifying them as image features that are easy to analyze. Then, comprehensive image features of retroreflectors need to be comprehensively analyzed, so as to realize detection activities such as detection, location, and identification of retroreflectors. Therefore, the core in detection of retroreflective attributes is two processes: one is imaging and quantification of retroreflection characteristic, and the other is multifeature fusion based recognition. The detection principle of retroreflection characteristic is illustrated in Fig. 4.1.

© National Defense Industry Press 2023 X. Yang and Y. Hu, Photoelectric Detection on Derived Attributes of Targets, https://doi.org/10.1007/978-981-99-4157-5_4

147

148

4 Active Imaging Detection on Target Retroreflection Features

Fig. 4.1 Detection principle of retroreflection characteristic

Active imaging aims to capture retroreflection characteristic. It can achieve collection of target images, including quantification and collection of retroreflection characteristic and collection of other features. Quantification and modeling of retroreflection characteristic refers to quantifying retroreflection characteristic into image features that are easy to extract and analyze through mathematical modeling, and it belongs to theoretical derivation and design. It, in essence, is to reveal the parameter matching of imaging devices with natural light sources and retroreflection characteristic, which is a key to guiding the design of an active imaging system. Image analysis and feature extraction mainly include two aspects, namely image preprocessing and feature extraction, so as to provide a feature library for establishing the grading model of feature salience. Then, quantitative modeling of feature salience transforms feature salience into mathematical description that is convenient for calculation and statistics, so as to classify extracted features into various grades according to the salience levels and build the grading model of feature salience. Finally, the grading model is used to guide the feature selection and combination in retroreflector detection and identification, so as to improve the success rate and the precision and efficiency of machine learning.

4.2 Quantification and Realization of Retroreflection Features 4.2.1 Retroreflection Features Retroreflection characteristic can be described as follows: reflected light propagates along the direction of the light source and remains in a small solid angle. According to theoretical analysis, such characteristic enables 98% of reflected light to concentrate within the solid angle of 3° ~ 4° [1]. When the incidence angle is in a certain range, the characteristic remains stable, and the larger the allowable range of the incidence angle is, the better the retroreflection characteristic.

4.2 Quantification and Realization of Retroreflection Features

149

Retroreflectors that are attached to targets are retroreflective sheeting, which can be classified into micro-beaded and micro-prismatic ones according to the principle. The chapter takes the former as an example to introduce its reflection principle. The principle of micro-beaded retroreflective sheeting is shown in Fig. 4.2, which consists of five parts, including a surface layer, glass beads, an adhesive layer, a reflective layer, and a substrate. Glass beads are a novel silicate material. They have retroreflection characteristic when their diameter is smaller than 0.8 mm, which enables a refractive index of 1.9 ~ 2.1 [2]. When a light beam is incident on the micro-bead surface with any angle in a certain range, the high refractive index of micro-beads renders the light to focus on a special reflective layer, which reflects the light beam parallelly along the direction of the light source [3]. It is generally considered that the optimal retroreflection characteristic can be obtained using glass beads with a retroreflection index of 1.93 because the light focus falls on the inner surface of the micro-beads. According to different structures, retroreflective sheeting can be divided into three types, namely embedded-lens, encapsulated, and microprismatic ones. Retroreflective sheeting is classified into five grades according to the different retroreflection performance: the first grade is micro-prismatic retroreflective sheeting, the second grade is encapsulated one (generally called high-intensity retroreflective sheeting), the third grade is embedded-lens one (generally called super-engineering-grade retroreflective sheeting), the fourth grade is embedded-lens one (generally called engineering-grade retroreflective sheeting), and the fifth grade is embedded-lens one (generally called economic retroreflective sheeting). Strict industrial standards have been set in various countries for retroreflective sheeting, for which an important characterization parameter is the retroreflection coefficient. The coefficient of luminous intensity is defined as:

Fig. 4.2 Principle of micro-beaded retroreflective sheeting

150

4 Active Imaging Detection on Target Retroreflection Features

R=

I , E⊥

(4.1)

where I is the luminous intensity in the retroreflection direction (cd); E ⊥ is the illuminance oriented to the retroreflector direction and falling on the plane vertical to the direction of incident light (lx). The retroreflection coefficient is defined as: R, =

I R = , A AE ⊥

(4.2)

where A is the surface area of the retroreflector (m2 ); the unit of retroreflection coefficient is cd · lx−1 · m−2 . The larger the retroreflection coefficient is, the better the retroreflection performance. Figure 4.3 shows the optical measurement principle of the retroreflection coefficient. According to stipulations in the standard GB/18833-2002: the retroreflection coefficient of retroreflective sheeting used in highway signs shall be measured according to the principle and the measured value shall not be lower than the values stipulated in the table of corresponding grades. Under wet conditions, the retroreflection coefficient of number plates (viewing angle of 0.2◦ and incidence angle of −4◦ ) should not be lower than 80% of stipulated values in the table of corresponding grades. Tables 4.1 and 4.2 display the industrial standards corresponding to the first-grade and fifth-grade retroreflectors in China. The standards for the minimum retroreflection coefficients corresponding to the other grades are reduced successively between the two. The higher the grade is, the higher the minimum retroreflection coefficient and the better the retroreflection performance.

Fig. 4.3 Optical measurement principle of the retroreflection coefficient

4.2 Quantification and Realization of Retroreflection Features

151

Table 4.1 Industrial standards of the first-grade retroreflectors Viewing angles

Incidence angles

Minimum retroreflection coefficients (cd·lx−1 ·m−2 ) White

0.2°

0.33°

Yellow

Red

Green

Blue

Brown

− 4°

600

450

120

100

50

20

15°

450

320

85

80

40

15

30°

300

220

60

50

25

10

− 4°

360

250

60

60

25

15

15°

260

180

40

40

18

10

30°

160

110

25

25

10

6.0

Table 4.2 Industrial standards of the fifth-grade retroreflectors Viewing angles

Incidence angles

Minimum retroreflection coefficients (cd·lx−1 ·m−2 ) White

0.2°

0.33°

1°

Yellow

Red

Green

Blue

Brown

− 4°

50

25

8.0

5.0

3.5

2.0

15°

35

14

6.0

4.0

2.5

1.5

30°

18

10

3.5

2.0

1.0

0.7

− 4°

30

15

5.0

4.0

2.5

1.5

15°

21

11

1.2

3.0

1.8

1.0

30°

10

5.0

2.0

1.5

0.8

0.4

− 4°

4.0

2.0

0.8

0.5

0.4

0.2

15°

2.5

1.3

0.6

0.4

0.2

0.1

30°

1.5

0.8

0.4

0.2

0.1

Retroreflective sheeting in different colors only has retroreflection characteristic to the corresponding spectra. For example, blue retroreflective sheeting is only applicable to the blue-light spectrum; the white one is suitable for the whole visible spectrum. There is no black retroreflective sheeting because black materials hardly reflect light in the visible spectrum and even IR spectrum. There are six types of retroreflective sheeting, namely the white, yellow, red, green, blue, and brown ones. according to the colors, the retroreflection performance of which decreases successively at the same grade. Among them, white and yellow retroreflective sheeting materials obviously outperform retroreflective sheeting in other colors, particularly the white one. For example, the corresponding minimum retroreflection coefficient of first-grade white retroreflectors is 5 and 30 times those of the red and brown ones; the corresponding value of first-grade red retroreflectors is 3.75 and 22.5 times those of the yellow and brown ones. It is worth noting that runways on airports and water-surface aircraft platforms, and indication signs for helicopter landing all use white retroreflectors; the indication and anti-collision signs on motion platforms and cave depots of garages, hangars, and boathouses also mainly combine white and yellow retroreflectors, which provide convenience for retroreflector detection. Although the grade

152

4 Active Imaging Detection on Target Retroreflection Features

of retroreflectors is divided in different ways in various countries, they all require to further enhance detectability and discernibility of the retroreflection characteristic.

4.2.2 Quantitative Modeling of Retroreflection Features Retroreflection characteristic is a cooperative optical property. How to capture the characteristic and at the same time reduce influences of common optical properties is a key to quantification of retroreflection characteristic. Comprehensively considering the spectral selectivity and directivity of retroreflection characteristic, retroreflection characteristic is quantified through directional active imaging under condition of spectral filtering. Active imaging achieves high-brightness display of retroreflectors and lowbrightness display of background by virtue of retroreflection characteristic of retroreflectors that is different from that of the target itself and background, thus providing a high-quality input source for retroreflector detection and identification. Therefore, it first needs to effectively inhibit interference of external light sources. In the daytime, the external light source is mainly sunlight, which forms direct sunlight and diffused skylight when it propagates through the atmosphere. The composition and proportion of the two types of light vary with different weather conditions and time moments. Diffused light is dominant in the morning and evening; it is all diffused light in cloudy weather; direct sunlight is dominant in sunny days except for the morning and evening. It can be seen from Tables 4.1 and 4.2 that the retroreflection coefficient is sensitive to the viewing angle. For white materials, the retroreflection coefficient corresponding to the viewing angle of 0.2◦ is 12.5 times of that corresponding to 1◦ when the incidence angle is −4◦ . Hence, when the solar trajectory is near the connection line between a retroreflector and an imaging sensor in sunny days, the sunlight has the most extreme interference to the active imaging. Figure 4.4 displays the schematic diagram for active imaging of a retroreflector. The intensity of diffused skylight is much lower than that of direct sunlight in this extreme case. An equivalent light source of diffused skylight is introduced in the same direction of the sun, which has the same illuminance to that produced by diffused skylight on the retroreflector surface, that is, the illuminance produced on the retroreflector surface by the sky over a quarter of spherical body in the figure. Under the condition, as long as the external light source is effectively suppressed, that is, component I1 from the active light source in luminous intensity in the retroreflection direction is greater than component I2 of direct sunlight and the equivalent light source of diffused skylight, the active imaging device can work normally in any time frame. Because the imaging device works in a certain wave band, the two components are both defined in corresponding wave bands. According to the definition of the retroreflection coefficient, the difference between the two components is ΔI = I1 − I2 = A[R1 E 1⊥ − R2 (E 2⊥ + E 3⊥ )],

(4.3)

4.2 Quantification and Realization of Retroreflection Features

153

Fig. 4.4 Schematic diagram for active imaging of a retroreflector

where A is the area of a retroreflector; R1 and R2 are retroreflection coefficients separately corresponding to the viewing angle of active light source and the sunlight viewing angle; E 1⊥ , E 2⊥ , and E 3⊥ are illuminances separately of the active light source, direct sunlight, and equivalent light source of diffused skylight oriented to the retroreflector and falling on the plane vertical to the direction of incident light, and they are determined using the following equations: ⎧ ⎪ ⎨ E 1⊥ = cos θ1 E 1 E 2⊥ = cos θ2 E 2 , ⎪ ⎩ E 3⊥ = cos θ2 E 3

(4.4)

where θ1 is the angle between the retroreflector surface and the plane vertical to the incidence direction of the active light source, that is, it is equal to the sum of the shooting angle and the viewing angle of active light source; similarly, θ2 is the sum of the shooting angle and sunlight viewing angle; E 1 , E 2 , and E 3 separately denote illuminances of the active light source, direct sunlight, and equivalent light source of diffused skylight on the retroreflector surface. Equation (4.3) is expressed as ΔI = A R1 cos θ1 E 1 − A R2 cos θ2 (E 2 + E 3 ).

(4.5)

In the extreme case considered, there is θ1 ≈ θ2 when the solar trajectory is near the connection line between the retroreflector and the imaging sensor. Equation (4.5) is further simplified as

154

4 Active Imaging Detection on Target Retroreflection Features

ΔI = A cos θ1 [R1 E 1 − R2 (E 2 + E 3 )].

(4.6)

In image acquisition equipment, it is obviously unpractical to use a light source that has a spectrum covering the whole visible-light band and can effectively inhibit the sunlight. However, it is much easier to obtain such light sources in a narrow band, such as high-energy flashlamps and laser illuminators. Therefore, E 1 , E 2 , and E 3 are all effective values of the working bands of imaging equipment. In active imaging that effectively suppresses external light sources, it requires that ΔI > 0. Therefore, the condition model of narrow-band active imaging based on retroreflection is / R1 E 1 R2 > (E 2 + E 3 ).

(4.7)

Diffused light is produced by the scattering of sunlight by air molecules, water vapor, and dusts in the atmosphere. The ground illuminance of diffused light is mainly influenced by the cloudage and cloud form in the sky and the impurity content in the atmosphere. Under sunny and cloud-free conditions, the illuminance is mainly determined by Rayleigh scattering by air molecules. Rayleigh scattering under the condition is mainly concentrated in ultraviolet and blue regions [4], while the red-light and near-infrared (IR) regions contribute little. Therefore, when performing red-light or near-IR narrow-band imaging, the above condition model is further simplified as: / R1 E 1 R2 > E 2 .

(4.8)

/ The above model indicates that increasing R1 R2 or E 1 is always favorable for improving the active imaging effect, while increasing the two retroreflection coeffi/ cients is more easily realized than enlarging R1 R2 . On the one hand, the viewing angle of the active light source can be reduced as far as possible, that is, decreasing the distance from the center of the light source to the center of the camera lens as far as possible, so as to improve R1 . On the other hand, the sunlight viewing angle can be enlarged as much as possible. For example, the installation position of imaging equipment can be adjusted, so that the connection line between the retroreflector and the imaging sensor is deviated from the plane of solar trajectory; or the imaging equipment can be installed below the carrying platform to effectively shelter from direct sunlight and some diffused skylight, thus remarkably reducing R2 . Because the retroreflection coefficient is highly sensitive to variation of the viewing angle, / the both methods can not only effectively enlarge R1 R2 but also decrease requirements for the brightness and power of the active light source. For example, when R1 /R2 = 20, active imaging quantification can be achieved as long as the illuminance of the active light source on the retroreflector is about one twentieth that of the sun.

4.3 Retroreflector Detection and Extraction

155

4.2.3 Analysis of the Active Imaging System The core of an active imaging system for retroreflector detection includes three parts: an active light source, a spectral filter, and an imaging sensor. The appropriate parameter matching of the three is a key to meeting the quantitative model of retroreflective characteristic. The narrow-band brightness of the active light source needed should be comparable to the sunlight. However, existing auxiliary light sources, such as iodinetungsten lamps, LED lamps, and IR lamps cannot satisfy the requirement. Compared with these light sources, high-energy flashlamps and laser illuminators have significant advantages in the aspect. Laser illuminators themselves are high-quality highenergy narrow-band light sources, while high-energy flashlamps can reach a high intensity instantly. Added with corresponding narrow-band filters, they can serve as effective high-energy narrow-band light sources. However, high-energy flashlamps have a large spectral range and belong to incoherent light sources, with relatively dispersed energy. Therefore, they only show a short action range and are applicable to situations shorter than 100 m. In comparison, laser illuminators are high-energy coherent light sources with concentrated spectral energy, which enables long-range operation, so they are more applicable to the active detection and reconnaissance fields [5]. For the selection of specific active laser sources, factors including the power, load, technical maturity, and spectral characteristics of retroreflectors can be considered comprehensively. Spectral filters are generally narrow-band filters, whose design follows the principle of maximizing the energy ratio of laser to sunlight in the narrow-band range on the precondition of guaranteeing the minimum-exposure imaging of the imaging sensor. Then, if multiple sensors are used or the sensor works in multiple wave bands, it needs to ensure spectral filters to work in relatively independent ranges. As for the imaging sensor, it needs to have high sensitivity in the working band of the active light source, so as to ensure the image quality and the match between exposure time and laser pulse width. Therefore, high requirements are set for exposure time, electronic shutter, and synchronous acquisition control [6].

4.3 Retroreflector Detection and Extraction 4.3.1 Analysis of Feature Salience Feature extraction and selection is a core step in target identification. Feature selection is more important than selection of classification methods. When extracting and selecting features, it is infeasible to take all target features into consideration in practice because of three causes: firstly, target images are also influenced by many random factors, such as potential unknown targets, target occlusion, target motion, environment variation, and noise jamming, so that target features are incomplete. Secondly, lots of features, such as gray, edge, and structural features are present for target

156

4 Active Imaging Detection on Target Retroreflection Features

images at different scales taken using different sensors. Extraction of these features will take a long time in computation. Thirdly, different features have difference in importance and reliability in target identification. The more salient the features are, the more accurate the identification results, while unsalient features generally only play a subsidiary role in identification. Therefore, target identification based on the perception process of feature salience can make full use of characteristics of target identification applications and improve the real-timeliness, intelligence, and applicability of target identification. Salience is human’s perception for the measure of quality or quantity of objects. For target identification, significance is the fundamental feature that distinguishes a target from other objects. For example, when judging two persons according to their voice, their intonations (feature 1) are differentiated at first. When the intonations of the two differ so slightly (low confidence) that cannot be accurately differentiated (classified), the two persons can be distinguished according to their intensity of sound (feature 2), until correct differentiation. The example suggests that the voice intonation is the most salient feature, while the intensity is the secondarily salient feature. Therefore, perceptual salience should have these characteristics: firstly, it should make one class of targets be separable from other targets and be a representative feature; secondly, the perception is independent of the measurement unit of features. Instead, it is a measure for the contribution of feature components to correct classification and it enables classification of features into the most salient, secondarily salient, and generally salient features. The traditional feature selection method is to select the optimal subset containing d features from D features. However, a same weight is assigned by the system to selected d features, which does not show the priority of these features. These features have the same weight in target identification. In fact, these d features still have priority, that is, feature salience. The more salient a feature is, the more effectively and greatly the feature contributes to target identification; similarly, the more unsalient the feature is, the lower its contribution to target identification. Given the both parameters, when selecting d optimal features from D features, the number of all possible combinations is Q = C Dd =

D! . (D − d)!d!

(4.9)

If D = 100 and d = 10, then Q is at the order of magnitude of 1013 ; if D = 20 and d = 10, then Q is 184,756. The computational amount is tremendous if considering all possible feature combinations and comparing them based on various indices and parameters. In addition, the number d of features in the optimal feature set to be selected is unknown in practical problems, so it has become an international research hotspot to develop the feasible feature selection algorithm. When there are two and more classes, feature selection turns to selection of features that are most effective for class separability. Therefore, the criterion for class separability can serve as the judgment basis for feature effectiveness.

4.3 Retroreflector Detection and Extraction

157

4.3.2 Modeling Based on Feature Salience How to measure salience is a problem pertaining to the measure criterion of salience. When it is applied to target identification, target feature modeling is to perform inductive learning under the given similarity criterion, while target feature classification is to conduct deductive reasoning under the given similarity criterion. They share a common point in similarity, that is, samples with certain common or similar features are classified into the same class, so as to minimize the misclassification risk. To this end, two salience measure criteria are defined: structural salience and probability-based salience.

4.3.2.1

Structural Salience

Intuitively, patterns that are distributed densely in the same class while far apart in different classes in a feature space can be easily classified and identified. Therefore, when selecting target features, it requires that selected features to differ significantly in different classes of objects while differ slightly in the same class, which provides convenience for the subsequent classification and identification. To reach the above goal, a criterion for feature selection and extraction needs to be formulated at first. Functions that reflect the intra-class and inter-class distances can be used as the criteria. To explain the concept of structural salience, several distance criterion functions are defined at first: (1) Intra-class distance criterion It is assumed { } that a pattern set {x1 , x2 , . . . , x N } to be classified is divided into c ( j) on the basis of a certain similarity measure, in which j = 1, 2, . . . , c class xi represents classes, i = 1, 2, . . . , n i is the serial number of intra-class patterns, and Σc j=1 n j = N . Intra-class distance criterion function JW is defined as: n j || c Σ ||2 Σ || ( j) || JW = ||xi − m j || ,

(4.10)

j=1 i=1

where

Σn j ( j) xi , j = 1, 2, . . . , c. m j is the mean value of patterns in class ω j , m j = n1j i=1 Equation (4.10) represents the sum of squares of class-center distances from a pattern set to the class that it is judged to. The lower the JW is, that is, JW → min, the more salient the features. (2)

Inter-class distance criterion

Corresponding to the intra-class distance criterion, maximization of the total interclass distance can be used as the feature selection criterion. The inter-class distance criterion is defined as:

158

4 Active Imaging Detection on Target Retroreflection Features

JB =

c Σ ( )( ) mj −m mj −m ,

(4.11)

j=1

where mj is the mean value of patterns in class ω j ; nj is the number of patterns in Σn j ( j) ΣN xi ; m is the total mean of patterns; m = N1 i=1 xi . The class ω j ; mj = n1j i=1 larger the JB is, that is, JB → max, the more salient the features. (3)

Intra-class and inter-class distance criterion

Sometimes, it requires features to enable the intra-class distance as small as possible while the inter-class distance of various classes as large as possible. In view of this, a criterion function that can simultaneously reflect the intra-class and inter-class distance is constructed. The criterion function JT for the total intra-class and inter-class distance is defined as: JT =

JB → max. JW

(4.12)

The features that enable a larger inter-class distance JB while a smaller intra-class distance JW , that is, the larger total intra-class and inter-class distances are more salient. Taking a binary classification problem as an example, assuming that the distribution function of a certain feature T of two classes in target identification is shown in Fig. 4.5, the mean values of features in the first and second classes are m 1 and m 2 , and their variances are σ1 and σ2 , then the intra-class and inter-class distance criterion is described as follows: JT =

Δm 2 (m 1 − m 2 )2 = → max . σ12 + σ22 σ12 + σ22

(4.13)

The larger the Δm is, the smaller the σ1 and σ2 , the lower the misclassification probability, and corresponding the higher the correct classification probability. This

Fig. 4.5 Distribution function of feature T in two classes of patterns

4.3 Retroreflector Detection and Extraction

159

indicates that feature T has a high probability to correctly distinguish the two classes and the classification results of target feature T are reliable. Therefore, the feature should be used in priority as the feature data for judgment. On the contrary, the smaller the Δm is, the larger the σ1 and σ2 , the higher the misclassification probability, and at the same time, the lower the correct classification probability. This indicates that feature T only has a low probability to correctly classify the two classes and the classification results of target feature T are less reliable. Structural salience has an advantage that it only needs a small sample size for learning, while its disadvantage is the low accuracy. When only a few samples are available, structural salience can be adopted for learning.

4.3.2.2

Probability-Based Salience

Due to background interference and limitation of imaging conditions, real-time features of targets are highly uncertain. Instead of a deterministic function, a salience measure criterion should be an empirical statistic of perception. The most salient feature corresponds to the largest probability; similarly, a relatively salient feature corresponds to a relatively large probability. Therefore, the minimumprobability-of-error (MPE) criterion is adopted to describe the feature salience [7]. According to the pattern recognition theory [8], when patterns to be recognized are judged to belong to a certain class, it is possibly misjudged. This means that the judgment results may be wrong when using the statistical criterion to judge the class of a certain pattern x. For example, a pattern that actually belongs to class ωj is probably judged to class ωi . For binary classification problems, the basic method of statistical judgment is to divide the feature space of patterns into two subregions Ω1 and Ω2 according to the probability and probability density of classes, that is, Ω1 UΩ2 =Ω, Ω1 ∩ Ω2 = ∅,

(4.14)

where ∅ is an empty set. When x ∈ Ω1 , it is judged that x ∈ ω1 ; when x ∈ Ω2 , it is judged that x ∈ ω2 . Here, two mistakes may happen: one is to judge a pattern actually belonging to class ω1 to class ω2 because the pattern belonging to ω1 is dispersed to class ω2 in the feature space, so that it is misjudged to belong to class ω2 . Under the condition, the misjudgment probability is { ε12 = Ω2

p(x/ω1 )dx.

(4.15)

160

4 Active Imaging Detection on Target Retroreflection Features

Similarly, the other mistake is to judge a pattern that actually belongs to class ω2 to class ω1 , when the misjudgment probability is { ε21 =

/ p(x ω2 )dx.

(4.16)

Ω1

Supposing that the occurrence probabilities of ω1 and ω2 are separately P(ω1 ) and P(ω2 ), then the total misjudgment probability P(e) is P(e) =P(ω1 )ε12 + P(ω2 )ε21 { { / / =P(ω1 ) p(x ω1 )dx + P(ω2 ) p(x ω2 )dx. Ω2

(4.17)

Ω1

Minimum misjudgment is expected generally and statistically, so the judgment criterion used is to minimize the misjudgment probability, which is equivalent to maximization of the correct classification and identification probability P(c), that is, { P(c) =

/ P(ω1 ) p(x ω1 )dx +

Ω1

{

/ P(ω2 )p(x ω2 )dx → max.

(4.18)

Ω2

This is the MPE criterion. In many cases, it is difficult to calculate the MPE. The calculation is more complicated particularly when the two classes do not follow the normal distribution. Here, a MPE estimation method under the general distribution condition is adopted (Fig. 4.6). For the binary classification problem, the MPE is

Fig. 4.6 Schematic diagram for the misjudgment probability of one-dimensional patterns

4.3 Retroreflector Detection and Extraction

⎡ P(e) = min⎣ P(ω1 )

{

161

{

/

p(x ω1 )dx + P(ω2 )

Ω2

/

⎤

p(x ω2 )dx⎦

Ω2

⎧ ⎫ 2 { ⎨Σ / ] ⎬ [ = min p(x) − P(ωi )p(x ωi ) dx ⎩ ⎭ i=1 Ω i { { [ / / ]} / = 1 − max P(ω1 x), P(ω2 x) p(x ωi )dx.

(4.19)

Ωi

/ / Because P(ω1 x) + P(ω2 x) = 1, there is / / ] [ / / ] [ 1 − max P(ω1 x), P(ω2 x) = min P(ω1 x), P(ω2 x) .

(4.20)

According to Eqns. (4.19) and (4.20), the expected value of MPE is / / ]} { [ P(e) = E min P(ω1 x), P(ω2 x) .

(4.21)

| [ / / |] / / ] [ Because min P(ω1 x), P(ω2 x) = 21 1 − | P(ω1 x) − P(ω2 x)| , there is P(e) =

1 2

{ Ω

{

[ / / ]} 1 1 − P(ω1 x) − P(ω2 x) p(x)dx = (1 − ρ), 2

/ / |] [| where ρ=E | P(ω1 x) − P(ω2 x)| . Because |tanh α| = tanh|α|, there is | | / / / | 1 || P(ω1 )P(x ω1 ) || || / tanh |ln | = P(ω1 x) − P(ω2 x)|. 2 | P(ω2 )P(x ω2 ) |

(4.22)

(4.23)

If there are N training samples xi (i = 1, 2, . . . , N ) of known classes, the arithmetic mean can be used to approximate the mathematical expectation, so there is | | / N 1 Σ 1 || P(ω1 )P(x ω1 ) || / ρ ≈ ρ= tanh |ln |. N i=1 2 | P(ω2 )P(x ω2 ) |

(4.24)

Then P(e) ≈

1 (1 − ρ). 2

(4.25)

162

4 Active Imaging Detection on Target Retroreflection Features

The judgment/rule under the MPE criterion is. / If P(ω1/)P(x ω1 ) > P(ω )P(x ω ), then it is judged that x ∈ ω1 ; if 2 /2 P(ω1 )P(x ω1 ) < P(ω2 )P(x ω2 ) , then it is judged that x ∈ ω2 ; this is equivP(x / ω1 ) 2) > P(ω , then it is judged that x ∈ ω1 ; alent to the following: if l12 (x) = P(x P(ω1 ) / ω2 ) P(x / ω1 ) P(ω2 ) if l12 (x) = P(x ω2 ) < P(ω1 ) , then it is judged that x ∈ ω2 and l12 (x) is called the / likelihood ratio. Due to different misjudgment costs of different objects to be identified, the concept of loss cost can be introduced. For the binary classification problem, when x is judged to belong to class ω1 , the average loss R1 is { R1 (x) = λ11 P(ω1 )

{

/

p(x ω1 )dx + λ21 P(ω2 )

Ω1

/ p(x ω2 )dx.

(4.26)

Ω1

When x is judged to belong to class ω2 , the average loss R2 is { R2 (x) = λ12 P(ω1 ) Ω2

/ p(x ω1 )dx + λ22 P(ω2 )

{

/ p(x ω2 )dx,

(4.27)

Ω2

where the loss factor λi j represents the loss caused by judging a pattern that actually belongs to class i to class j. For retroreflector detection, the misjudgment cost is identical, that is, λi j (i = j ) = 0 and λi j (i /= j) = 1. Evidently, the criterion of minimum loss is equivalent to the MPE criterion, and minimizing the loss is to minimize the misjudgment probability. Therefore, the MPE criterion analysis can be applied to the number-plate recognition system proposed in the chapter. In the feature extraction process, as many as non-exclusive features {T 1 , T 2 , …, T n }, including gray, variance, and edge should be extracted. Then, the distribution density function f (Ti ) of two classes corresponding to each feature Ti (i = 1, 2, …, n) is solved individually and successively. The distribution forms of density functions of Ti are shown in Fig. 4.7. According to the distribution curves of density functions of the feature Ti ({1, 2, · · ·, n}) in the first and second classes, the corresponding MPEs Pe (Ti ) are calculated. After statistics of all features, the MPE set corresponding to the feature Then, these MPEs are ranked to attain set { Pe (T1 ), Pe (T2 ), {…, Pe (Tn )} is obtained. } a feature sequence T1, , T2, , · · ·, Tn, ranked in a descending order by the salience according to the criterion that the smaller the MPE is, the larger the feature salience. The probability-based salience based on the MPE has the advantage of high accuracy while disadvantage of necessity of a large sample size for learning. When numerous samples can be obtained, features can be selected according to the probability-based salience.

4.3 Retroreflector Detection and Extraction

163

Fig. 4.7 Distribution of density functions of feature Ti

4.3.3 Target Detection Based on Feature Salience Considering the high precision of the model of feature salience based on the MPE, the model is used to quantitatively describe salience of multiple features of retroreflectors and grade these features according { } to their salience. In this way, the grading model of feature salience T1, , T2, , · · ·, Tn, is established. Information fusion, to a large extent, equivalently expands the temporal and spatial coverage, decreases information fuzziness, increases reliability, improves detection performance, and increases the target identification probability. Therefore, when designing the target detection algorithm through multifeature fusion, the grading model of feature salience can guide design of the fusion weight or priority. Feature fusion can be divided into two modes, namely the parallel and serial ones. Parallel feature fusion refers to assigning different weights to all features according to their salience and then fusing these features; serial feature fusion means successively inputting the most salient and secondarily salient features according to the feature salience for fusion. Figure 4.8 shows the flow chart of different feature fusion modes, in which T1 , T2 , · · ·, Tn separately represents various salient features and F1 , F2 , · · ·, Fn separately denote various fusion centers.

4.3.3.1

Parallel Feature Fusion Based on Feature Salience

Parallel feature fusion based on feature salience is displayed in Fig. 4.9.

164

4 Active Imaging Detection on Target Retroreflection Features

Fig. 4.8 Schematic diagram for serial and parallel feature fusion

Fig. 4.9 Schematic diagram for parallel feature fusion based on feature salience

According to the above feature salience analysis, the feature salience sequence is obtained. A larger weight is assigned to more salient feature. Assuming that there are m-dimensional salient features in n classes of targets, the following decision-making function is obtained after normalizing various features: ⎡

⎤ ⎡ P1 T11 ⎢ P2 ⎥ ⎢ T21 ⎢ ⎥ ⎢ ⎢ . ⎥=⎢ . ⎣ .. ⎦ ⎣ .. Pn

T12 · · · T22 · · · .. .

⎤⎡ ⎤ W1 T1m ⎢ ⎥ T2m ⎥ ⎥⎢ W2 ⎥ .. ⎥⎢ .. ⎥, . ⎦⎣ . ⎦

Tn1 Tn2 · · · Tnm

Wm

(4.28)

4.3 Retroreflector Detection and Extraction

165

where ( P1 , P2 , · · ·, Pn ) represents n confidence probability functions; (T1 , T2 , · · ·, Tm ) is salient features; (W1 , W2 , · · ·, Wm ) is weight coefficients of various salient features. When Pi = Arg max ( P1 , P2 , · · ·, Pn ), it is judged that the pattern X ∈ ωi .

4.3.3.2

Serial Feature Fusion Based on Feature Salience

The basic principle of the target detection method based on grading of salient features is to build a model of graded features of targets and background based on salience on the basis of analyzing feature salience of targets and background. The reliability of target classification is estimated through recursion of graded features, thus achieving target detection and improving the target detection and identification speed. Target detection and identification is actually a binary classification problem of targets and background. Due to influences of the scene of targets, unknown targets, and random noises, the images contain background interference of indefinite features apart from targets of definite features. Therefore, the model of graded features of targets and background based on salience inevitably has some uncertain. Considering this, the research focus of this problem is the recursive estimation methods and algorithms for the confidence of the model of graded features of targets and background based on salience, which are key to ensuring the effectiveness and reliability of target detection and identification. Dempster-Shafer (D-S) evidence theory is a well-studied evidential combination (feature fusion) method. The evidential combination rule of the D-S evidence theory is improved using the MPE in the chapter and then applied to serial feature fusion. (1) The improved evidential combination rule of the D-S theory based on the MPE [9] When assigning confidence functions to a proposition according to evidence distribution in practice, it is affected by many factors and different methods may induce different basic probability assignment (BPA) formulas, which need to be determined according to specific conditions. Generally, the BPA should be constructed according to factors such as the importance and reliability of features for the measurement and decision-making of targets. In the evidential combination rule of the D-S theory, evidences from various information sources are treated equally. Under conditions of different reliability and importance levels of various information sources, the evidential combination rule of the D-S theory may yield combination results not confirming to the objective situation. Therefore, a weighted confidence assignment function based on the MPE is proposed here on the basis of previous research on feature salience based on the MPE. The more salient a feature is, the more effective the pattern classification, the smaller the misclassification probability Pi (e), and the larger the [1-Pi (e)]. Therefore, the weight coefficient of various information sources can be constructed.

166

4 Active Imaging Detection on Target Retroreflection Features

αi =

[1 − Pi (e)]

q Σ

,

(4.29)

[1 − Pi (e)]

i=1

Σq where q is the number of information sources and meets i=1 αi = 1. The BPA function m i (i = 1, 2, . . . , q) can be modified as m i, = αi m i (i = 1, 2, . . . , q).

(4.30)

How to make decisions after combining evidences using the evidence theory is a problem closely related to applications. Different decision-making methods can be used according to different needs. (3) Decision-making based on the belief function. The belief function bel is solved according to m obtained after combination. The belief function is the judgment results. To narrow the truth-value range, the principle of minimum point can be used to calculate the truth value. The principle of minimum point means that for a set A and a belief function bel(A), if the belief is bel(B1 ) and |bel(A)-bel(B1 )| ε1 . m(U ) < ε2 ⎩ m(A1 ) > m(U )

(4.33)

If there are

Then A1 is the judgment results, for which ε1 and ε2 are the pre-set thresholds. Analysis reveals that it seems better to not judge classes if class attributes of some patterns are not obvious. That is to say, for n-class problems, the number of decision schemes in the decision set can be larger than that of classes, which means that it is feasible to not make a certain decision in the decision set, which corresponds to the uncertainty interval in decision-making. The binary classification problem needs to be considered at first in a bid to reveal the relationship between the MPE and confidence. Near the likelihood ratio threshold, it is unreliable to decide the class of pattern x. To improve the reliability of judgment, two likelihood ratio thresholds

4.3 Retroreflector Detection and Extraction

167

M and N that meet N < M can be set, which is equivalent to dividing the feature space into three subregions and adding an uncertain region between the two decision regions. If the number of features is not enough and the class attributes of pattern x to be recognized are not salient, x falls in the uncertain region Ω 3 . By adding features, the pattern enters Ω 1 or Ω 2 , thus judging the class of the pattern. One of the important tasks of uncertain reasoning is to gradually narrow the uncertain region. A total of k features x 1 , x 2 , …, x k in n features are fused. Letting x(k) = (x 1 , x 2 , …, x k ), the likelihood ratio of k features can be considered. | P(x1 ,x2 , · · · , xk |ω1 ) def P( x (k) |ω1 ) (k) | = l12 (x ) = . (4.34) P(x1 , x2 , · · · , xk |ω2 ) P( x (k) |ω2 ) So, the judgment rule is. (1) l12 (x (k) ) ≥ M → x ∈ ω1 (2) l12 (x (k) ) ≤ N → x ∈ ω2 (3) M ≤ l12 (x (k) ) ≤ N → x ∈ Ω3 , that is, judgment cannot be made and features need to be added for the pattern to continue identification. To determine thresholds M and N, letting e12 be the probability that x actually belongs to ω1 while x ∈ ω1 is negated; letting e21 be the probability that x actually belongs to ω2 while x ∈ ω2 is negated. The rejection region of ω2 is | { } (m) Ω10 = x (m) | M ≤ l12 (x (k) ) ≤ N , k = 1, 2, . . . , m − 1, l12 (x (m) ≥ M) . (4.35) The rejection region of ω1 is | { } (m) Ω20 = x (m) | N < l12 (x (k) ) < M, k = 1, 2, . . . , m − 1, l12 (x (m) ≤ N ) . (4.36) Therefore, e21 ≤

∞ { Σ k=1

| ) ( M −1 p x (k) | ω1 dx (k) .

(4.37)

(k) Ω10

Thus, / M ≤ (1 − e12 ) e21 .

(4.38)

/ N ≥ e12 (1 − e21 ).

(4.39)

Similarly, there is

Inequalities (4.38) and (4.39) show that M and N are related to e12 and e21 , that is, they are controlled by selection of the two error probabilities. To ensure that the

168

4 Active Imaging Detection on Target Retroreflection Features

judgment has the selected two error probabilities e12 and e21 , M and N must meet Inequalities (4.38) and (4.39). / M=(1 − e12 ) e21 ,

(4.40)

/ N = e12 (1 − e21 ).

(4.41)

They are called the termination limits. During judgment using the above equations, the real error probabilities are e12 and e21 . Inequalities (4.38) and (4.39) have the following relationships. / / (1 − ε12 ) ε21 ≥ (1 − e12 ) e21 ,

(4.42)

/ ε12 (1 − ε21 ) ≥ e12 (1 − e21 ).

(4.43)

Inequalities (4.42) and (4.43) contain / ε12 ≤ e12 (1 − e21 ),

(4.44)

/ ε21 ≤ e21 (1 − e21 ).

(4.45)

Because e12 and e21 are small, when e12 or e21 is greater than ε12 or ε21 , the difference will not be too big. Inequalities (4.44) and (4.45) are arranged and then added, thus obtaining ε12 + ε21 ≤ e12 + e21 .

(4.46)

Inequality (4.46) shows that the upper bound of the sum ε12 + ε21 of the two error probabilities is the sum e12 + e21 of design indices e12 and e21 , and at least one of the inequalities ε12 + e21 and e12 + ε21 is established. The two inequalities are generally both established. The update process of the confidence in the D-S evidence theory is shown as the serial feature fusion based on salience in Fig. 4.10. When the confidence exceeds a certain threshold, the confidence update process can be stopped. In the fusion and identification process, the affirmative evidence (or reliability) of targets is cumulative. For multifeature target identification, the fusion and identification using as many non-exclusive features as possible can yield more reliable results. However, it is impossible to extract complete real-time features for identification due to influences of the time and imaging conditions. Instead, several most salient features can only be selected according to the actual condition. Based on the measure criterion of salient features, a feature selection method from primary to secondary ones is adopted. Features of different saliences are assigned with different

4.3 Retroreflector Detection and Extraction

169

Fig. 4.10 Schematic diagram for serial feature fusion based on feature salience

probability weights and used for fusion and identification, which is a dynamic identification process. The identification ends when the confidence of targets exceeds a certain threshold or the confidence is still lower than the threshold after fusion and identification by extracting a certain number of features. The method does not require to extract all features contained in candidate targets and the image interpretation process is driven by the most salient feature. Through fusion of several features, the feature extraction and the identification evaluation process are linked. The types of target features extracted and the calculation algorithm are adjusted real-timely according to the confidence. Parallel feature fusion is to fuse information after obtaining all features; while serial feature fusion does not need to acquire all feature information before fusion. Serial feature fusion has the disadvantage of high sensitivity to the presence of each feature, while it has favorable performance and fusion effects. In addition, serial feature fusion is to combine the observed value of features at the J grade and the judgment results of fusion centers at the J − 1 grade, which are then transmitted to the fusion centers at the J grade to test the same assumption. The process is repeated until the judgment reaches certain given reliability. In this way, the judgment process is ended at any time, with no need to judge all feature combinations, which shortens the operation time and has low requirements for the computer.

170

4 Active Imaging Detection on Target Retroreflection Features

4.4 Retroreflector Detection Examples 4.4.1 Retroreflector Examples It is clearly stipulated in the safety industrial standard Retroreflective sheeting for number plate of motor vehicles (GA 666—2006) that, number plates shall be produced with retroreflective sheeting meeting certain standards and they are a typical type of retroreflectors. The minimum retroreflection coefficients of number plates are listed in Table 4.3. Number plates, as a part that is attached to vehicles, are targetattached attributes studied in the monograph. The section introduces the detection examples of such attribute [10] and the realization process is based on the theories elaborated in Sects. 4.1, 4.2, and 4.3. By doing so, the authors attempt to provide reference of practical significance for the detection and identification of targets to which other retroreflectors are attached. At present, number plates used in various countries mainly fall into four types: white characters on blue background, black characters on white background, black characters on yellow background, and white characters on black background. For number plates with black characters on yellow background and black characters on white background, only characters have retroreflection characteristic, while characters and background on number plates with white characters on blue background both have retroreflection characteristic [11]. Although retroreflectors in different colors have selectivity for the visible-light spectrum, they show favorable retroreflection characteristic for the near-IR spectrum. Figure 4.11 illustrates the visible-light and near-IR imaging results of number plates with white characters on blue background under conditions without interference of other light sources in the nighttime. A LED white-light source and a near-IR light source with the central wavelength of 850 nm that work independently are separately installed on the camera lenses. In the two images, the number-plate area is obviously brighter than the car body and other background. The blue background and white characters in the number-plate area of Table 4.3 Minimum retroreflection coefficients of number plates Viewing angles

Incidence angles

White

Yellow

Blue

Red

Green

0◦ 12,

5◦

60

40

4.0

14

9.0

30◦

25

18

1.5

6.0

3.5

45◦

6.0

4.0

0.5

2.0

1.0

5◦

40

30

3.0

10

7.0

30◦

14

10

1.0

4.0

2.5

45◦

3.0

2.0

0.3

1.0

0.8

5◦

4.0

3.5

0.6

2.0

1.0

30◦

2.0

1.5

0.2

0.6

0.4

45◦

0.7

0.5

0.1

0.2

0.1

0◦ 20,

1◦ 30,

4.4 Retroreflector Detection Examples

(a) Visible-light imaging

171

(b) Near-IR imaging

Fig. 4.11 Retroreflection of the number plate

the visible-light image exhibit the significant contrast, while the number-plate area of the near-IR image shows uniform gray level such that the background and characters have been fused together. Therefore, retroreflector detection can extend from the visible-light to the near-IR band.

4.4.2 Analysis and Design of the Active Imaging System 4.4.2.1

Establishment of the Imaging System

The color-discrete characteristic of number plates reveals that the background and characters on the four types of number plates also show the most significant difference in the reflection characteristic in the red-light band[12]. In addition, influences of red light on the visual sense of drivers are weaker than those of white light. Therefore, it is considered to use red-light narrow-band active imaging at first. Besides, only background has retroreflection characteristic on number plates with black characters on yellow background and black characters on white background; for number plates with white characters on black background, only characters show retroreflection characteristic. For them, near-IR narrow-band active imaging can also effectively highlight contrast of the number-plate area and the near-IR light has even less impairment to the visual sense of drives. Moreover, novel 02-type number plates with black characters on white background issued in China since 2002 are expected to replace other types of number plates in the future and provide a potential application space for near-IR narrow-band active imaging. Considering this, narrow-band imaging was separately conducted in red-light and near-IR bands, while the flashlamps were required to have high energy in the two bands. The spectral characteristics of the xenon flashlamp selected are shown in Fig. 4.12, which shows multiple significant peaks in the red-light and near-IR bands, providing convenience for the two types of near-band active imaging. The output optical power of the flashlamp reaches 500 W, beam angel is 30◦ , recycle time is 300 ms, flash rate reaches 3 times/s, and flash time

172

4 Active Imaging Detection on Target Retroreflection Features

reaches 1 ms, which is favorable for fast continuous capture. For red-light and nearIR active imaging, two narrow-band filters are designed and built in corresponding camera lenses. Therein, the central wavelength, band width, and peak transmittance of the red-light narrow-band filter are 680 nm, 20 nm, and 78% while those of the near-IR narrow-band filter are 820 nm, 26 nm, and 75%, respectively. To reduce influences of the flashlamp on the visual sense of drivers as far as possible and avoid influences on narrow-band active imaging, a 650-nm high-pass filter was designed and installed in the front of the flashlamp. The spectral characteristics of the three filters are displayed in Fig. 4.13. Therefore, the operating bands of the two types of narrow-band active imaging are separately 670 ~ 690 nm and 807 ~ 833 nm. A visible-light camera was used for red-light narrow-band active imaging, with a response band of 400 ~ 700 nm (with a built-in cut-off filter) and a relative response rate about 0.7 at 680 nm, as displayed in Fig. 4.14. The minimum illuminance is 0.01 lx. Common cameras have a low response rate at 820 nm, which is unfavorable for narrow-band imaging. Therefore, Fig. 4.12 Spectral characteristics of the xenon flashlamp

Fig. 4.13 Spectral characteristics of the filters

4.4 Retroreflector Detection Examples

173

a low-illuminance black-and-white camera was adopted, with a relative response rate greater than 0.4 at 820 nm (Fig. 4.15) and the minimum illuminance of 0.0003 lx. To suppress the background reflected light as much as possible, the two cameras have an electronic shutter speed of 1 μs and the synchronous trigger function of the flashlamp, thus ensuring high-speed capture within the flash time. The physical picture of the active imaging device is illustrated in Fig. 4.16. The near-IR and visible-light cameras are arranged vertically with a center-to-center distance of 5 cm of the two lenses. The active light source is installed in close vicinity of the two cameras, with the same center-to-center distance (about 10 cm) to the two lenses. The two cameras are equipped with visible-light and near-IR lenses with the same focal length of 8 mm and the diameter of built-in filters is slightly smaller than those of the lenses. The high-pass filter used in the front of the flashlamp is slightly larger than the lamp face. The lampshade and the outside of the filter have matched screw holes to provide convenience for installation, fixation, and disassembly. Besides, there are also a power supply and a data transmission and storage device inside the case. Fig. 4.14 Spectral characteristic of the visible-light camera

Fig. 4.15 Spectral characteristic of the near-IR camera

174

4 Active Imaging Detection on Target Retroreflection Features

Fig. 4.16 Physical picture of the active imaging devices

Whether the active imaging device can meet the condition model in Eq. (4.8) was estimated according to parameters of the imaging device and empirical illuminances of sunlight and diffused skylight in sunny weathers. Supposing that the active light source is a Lambert reflector with uniform brightness within the beam angel, then the total irradiance on the surface of number plates is E 1, =

P [ ( / )]2 , π d tan α 2

(4.47)

where P is the output optical power of the flashlamp; α is the beam angel; d is the distance from the flashlamp to the number plate. / It is estimated that the narrow-band energy in 670 ~ 690 nm is about 1 15 / of the total energy according to the spectral distribution, so E 1 ≈ E 1, 15. Based on the spectral irradiance distribution of sea-level sunlight, it is estimated that the narrow-band energy in 670 ~ 690 / nm is about 24 W/s [13]. Assuming that the shooting distance is d = 15, R1 R2/> 36.5 is obtained by substituting parameters into Eq. (4.8). Similarly, there is R1, R2 > 46.9 for near-IR narrow-band imaging. This requires that in extreme cases, the retroreflection coefficient of the active light

4.4 Retroreflector Detection Examples

175

source is about 47 times that of direct sunlight. However, the sunlight intensity cannot approximate the ideal condition even in sunny and wind-less weathers in practical applications. In the meantime, the viewing angle of the active light source at a range of 15 m is only 0◦ 23, , whereas the retroreflection coefficient is extremely sensitive to the viewing angle (see details in Table 4.3). By adjusting the installation position of the imaging device or adding a baffle plate, the retroreflection coefficient of direct sunlight can easily and rapidly meet the model requirement.

4.4.2.2

Imaging Experimental Analysis

The active imaging device was adopted to collect number-plate images under conditions of direct sunlight, direct illumination of headlights in the nighttime, and complex background, and the results were compared with the traditional image acquisition effect. Figure 4.17 shows the imaging effects under conditions with a viewing angle 5◦ ~ 10◦ of direct sunlight in sunny and wind-less weathers. In the image acquired through passive imaging, the number-plate area is clearly visible while the background noises have high complexity, with significant contrast. In the images acquired using the two narrow-band active imaging methods, the number-plate areas are highlighted against the car body and other background, and they are bright; while the background complexity reduces significantly, with only a few background areas that strongly reflect sunlight in the viewing direction. The image quality is improved significantly [11]. When acquiring number-plate images in the nighttime, a LED white-light source, near-IR lamp, or common flashlamp is generally used for supplementary lighting. Figure 4.18 shows the various imaging effects under direct illumination of headlights in the nighttime. Influenced by headlight glare, passive imaging can hardly obtain clear number-plate areas. Although traditional supplementary lighting modes can significantly improve brightness of the number-plate area, the headlight glare, reflection by road background, and other interfering light sources also form numerous high-brightness areas, which seriously interfere the number-plate recognition process. The two narrow-band active imaging modes almost eliminate all influences of reflection by road background and other interfering light sources while

(a) Passive imaging (b) Red-light narrow-band active imaging (c) Near-IR narrow-band active imaging

Fig. 4.17 Imaging effects under a viewing angle of 5◦ ~ 10◦ of direct sunlight

176

4 Active Imaging Detection on Target Retroreflection Features

improving brightness of the number-plate area. In addition, they remarkably lower the brightness of headlight glare, restrict the headlight areas within small ranges, and provide high-quality input sources for the number-plate recognition algorithm [14]. Moreover, narrow-band active imaging has incomparable advantages over passive imaging in overcoming complex background. Figure 4.19 illustrates the passive imaging effect on a double-line number plate, which not only contains a fence with extremely abundant textures, but also includes multiple groups of horizontally arranged regular characters. It is a challenge for the number-plate recognition algorithm to effectively distinguish these interfering character strings from the real number plate in the complex background. However, the two narrow-band active imaging modes almost only remain the clear number-plate area and obtain the amazing imaging effects, as displayed in Fig. 4.18b and c. The two images are collected under conditions without direct sunlight, which also indicates that the imaging effect will be further improved with the reduction of direct light and influences of the background can even be completely eliminated. It is worth noting that the retroreflective grade of number plates is equivalent to the fifth grade of highway signs. However, number plates are also well quantified under active imaging conditions, which is equivalent to further demonstrating the feasibility and necessity of applying the method to high-grade retroreflectors.

(a) Passive imaging (b) Supplementary lighting using a LED white light using a common near-IR lamp

(c) Supplementary lighting

(d) Supplementary lighting using a common flashlamp (e) Red-light narrow-band active imaging Near-IR narrow-band active imaging

Fig. 4.18 Imaging effects under direct illumination of headlights in the nighttime

(f)

4.4 Retroreflector Detection Examples

(a) Passive imaging

177

(b) Red-light narrow-band active imaging

(c) Near-IR narrow-band active imaging

Fig. 4.19 Imaging effects of a double-line number plate under complex background

4.4.3 Target Detection Through Multifeature Fusion Based on Feature Salience 4.4.3.1

Calculation of Feature Salience

The MPE criterion introduced in Sect. 4.3 was used to calculate the feature salience. Assuming that the probabilities for occurrence of classes ω1 and ω2 are separately P(ω1 ) and P(ω2 ), then the MPE calculation model is P(e) =P(ω1 )ε12 + P(ω2 )ε21 { { ( / ) ( / ) =P(ω1 ) p x ω1 dx + P(ω2 ) p x ω2 dx → min . Ω2

(4.48)

Ω1

According to Eq. (4.48), the total error probability P(e) consists of the prior probability and the probability density function. If regarding the target set as class Ω1 and the background set as class Ω2 , then P(ω1 ) and P(ω2 ) separately ( / ) represent ( / the) prior probabilities for occurrence of targets and background; p x ω1 and p x ω2 separately denote the probability distribution functions of targets and background in the images. How to obtain the two prior probabilities and the probability distribution functions of the two conditions is the key to calculating the MPE and quantitatively describing feature salience of retroreflectors. To this end, the artificial supervised learning method was used to artificially mark target areas in each image ) ( /for learning. ( Then, / ) the preliminary estimates of P(ω1 ) and P(ω2 ) as well as p x ω1 and p x ω2 were obtained, and the probability functions were fitted, so as to calculate the probability distribution function. In this way, after continuous learning of several of (images, ) ( / frames / ) more accurate estimates of P(ω1 ) and P(ω2 ) as well as p x ω1 and p x ω2 , and relatively accurate distribution functions can be obtained. A total of 200 frames of red-light images (1,280 × 960) of the car were acquired using the designed imaging device based on the experimental samples. Each image only contains a car and a number plate. The total block number is N with the block

178

4 Active Imaging Detection on Target Retroreflection Features

size of 8 × 8. At first, the target area (number plate) is artificially framed out. The number-plate area occupies m blocks, so the total number of blocks in background is n = N-m. Therefore, the prior probabilities of the number plate and background are separately P(ω1 ) =

n m , P(ω2 ) = . m+n m+n

(4.49)

Statistics histograms were drawn separately for the feature distribution of the number plate and background (brightness for instance), thus obtaining the statistical functions g1 (x) and g2 (x), which separately represent the numbers of blocks in the number plate and background corresponding to different gray levels, as shown in Fig. 4.20. They are further normalized, thus attaining the probability distribution functions of the two conditions: ( / ) ( / ) 1 1 p x ω1 = g1 (x), p x ω2 = g2 (x). m n

(4.50)

By substituting Eqns. (4.49) and (4.50) into Eq. (4.48) for calculating the MPE formula, the following is obtained ⎞ ⎛ { { 1 ⎝ g1 (x)dx + g2 (x)dx ⎠ . P(e) = m+n Ω2

(4.51)

Ω1

It is expected to have minimum misjudgment generally and statistically. According to the MPE criterion, the judgment criterion used is to minimize the misjudgment probability, which is equivalent to maximizing the correct classification and identification probability P(c), that is,

Fig. 4.20 Schematic diagram for calculating misjudgment probabilities of the number plate and background (probability P, area beyond the number plate, number plate)

4.4 Retroreflector Detection Examples

179

⎞ ⎛ { { 1 ⎝ P(c) = g1 (x)dx + g2 (x)dx ⎠ → max . m+n Ω1

(4.52)

Ω2

According to characteristics of the number-plate area under red-light active imaging conditions, three features including the brightness, connected area [15], and edge density [16] were extracted as analysis objects. Then, the MPEs of the three were calculated separately to be 0.397, 0.211, and 0.195 using the formula. Therefore, the grading model of salience is the brightness, edge density, and parallel frame.

4.4.3.2

Feature Fusion-Based Detection Algorithm

Under the red-light active imaging condition, the number-plate area is significantly brighter than the car body and other background. At the same time, much complex background is suppressed to a limited extent, as shown in Fig. 4.19b. The three features, namely the brightness, connected area, and edge density can well describe discernibility of the number-plate areas. Therefore, parallel feature fusion was adopted to design the multifeature fusion-based target detection algorithm. The above three salient features of number plates were collected to calculate estimates of fusion confidence for occurrence of targets. The corresponding features {I, N , D} were separately extracted. According to salience analysis of the brightness I , connected area N , and edge density D of number plates, the parallel fusion algorithm of features with different weights was used to select the only correct number plate to be identified from multiple candidate number plates (including number plates and areas beyond number plates). Supposing that there are n candidate number plates, their brightness, connected area, and edge density are separately defined as {I1 , I2 , I3 , · · ·, In }, {N1 , N2 , N3 , · · ·, Nn }, and {D1 , D2 , D3 , · · ·, Dn }. According to the above analysis, the brightness is known to be the most significant feature, while the connected area and edge density are secondarily salient features. The more salient the features are, the more greatly the identification confidence is characterized, while the less salient features exert small influences on the confidence. Considering this, the weight W1 of brightness should be larger than weights W2 and W3 of the connected area and edge density. The three weights meet the relationship of W1 > W2 > W3 , and W1 + W2 + W3 = 1. The more salient the features are, the more effective the pattern classification, the lower the misclassification probability Pi (e), and the greater the (1 − Pi (e)). Therefore, the MPE corresponding to features is used as the weight coefficient Wi of features. Wi =

(1 − Pi (e)) 3 Σ (1 − Pi (e)) i=1

i = 1, 2, 3.

(4.53)

180

4 Active Imaging Detection on Target Retroreflection Features

Σ3 The above equation meets i=1 Wi = 1. Assuming that the confidence of judging the ith candidate area as a real number plate is Pi (i = 1, 2, · · ·, n), then Pi =W1 m 1 (·) + W2 m 2 (·) + W3 m 3 (·) |Ni − N |−1 |Di − D|−1 |Ii − I |−1 =W1 Σ | |−1 + W2 Σ | |−1 + W3 Σ | | . |I j − I | |N j − N | | D j − D |−1 j

j

(4.54)

j

The normalized confidence is Pi Pi, = Σ , Pj

(4.55)

j

where j = 1, 2, · · ·, n. m l (·) is the confidence of judging an area as the numberplate area based on feature l(1, 2, 3). If the confidence is larger than T , then the area belongs to the number plate; if there are multiple such areas, they are merged as one. Because the number plate is unique in the test images, that is, the images only contain one number plate, it is the number-plate area that is output in the process.

4.4.3.3

Detection Experiments Based on Feature Fusion

A total of 200 red-light images (1280 × 960) of a car were collected using the quantification and active imaging system for retroreflectors designed in the chapter and used as test set 1. Each frame only contains a number plate. Under the same condition (including the same field of view), a common visible-light camera was adopted to acquire test set 2. That is, conditions including the target, background, and natural illumination are completely same in corresponding images, and it is the selected imaging devices that are different, as displayed in Fig. 4.21a and c. The test sets cover multiple complex conditions, including the complex background, weak illumination, uneven illumination, and incomplete characters. Four test algorithms were used, including the salience-based multifeature fusion proposed in the chapter, salience-based multifeature fusion proposed in [7], the one based on connected areas proposed in [15], and the one based on edge density proposed in [16]. The algorithm proposed in the chapter was only run in test set 1 because images in test set 2 did not pointedly quantify features of retroreflectors. The algorithm proposed in [7] was only run in test set 2 because of its need for color features. The other two algorithms were separately run in test sets 1 and 2 and they both can provide corresponding features. Success detection is the case without false alarm and with complete target area (character incompletion or occlusion can be ignored while normal characters have to be retained). The comparison of test results of different target detection algorithms is listed in Table 4.4. The two algorithms based on the connected area and edge density have

4.4 Retroreflector Detection Examples

181

(a) Occlusion in both sides

(b) Occlusion in the middle

(c) Occlusion in both sides

(d) Occlusion in the middle

Fig. 4.21 Detection of the occluded number plate

high success rates in the test set 1, which is 10% higher than that in the test set 2. This further verifies the significant enhancement effect on features in the number-plate area and inhibition effect on background noises of the quantification and imaging model of retroreflectors. It can be seen from Fig. 4.21a that number-plate images obtained through traditional visible-light imaging contains lots of noises, so that the only target area cannot be detected unless using features of high discernibility. It is difficult to achieve this point using methods based on a single feature. The detection will be more difficult in the case of uneven illumination and occlusion. In addition, the quantification and imaging model of retroreflectors fully highlights features in the number-plate area and substantially suppresses the background noises, thus significantly enhancing the contrast of the number-plate area relative to the background, as shown in Fig. 4.21b. In addition, active imaging overcomes adverse factors such as weak and uneven illumination and further lowers requirements for the detection algorithm. For such high-quality target images, the methods based on a single feature also have relatively high success rates. However, salience-based multifeature fusion obviously can better cope with complex conditions compared with those using single features. For the test set 2, the algorithm proposed in [7] also achieves a success rate of 93%, and the failure rate of 7% is mainly caused by the false alarm incurred by complex background

182

4 Active Imaging Detection on Target Retroreflection Features

Table 4.4 Comparison of test results of detection algorithms Algorithms

Feature types

Success rates (corresponding to the number of images) Test set 1

Test set 2

The algorithm in [15]

Connected area

92.5% (185)

82% (164)

The algorithm in [16]

Edge density

91.5% (183)

80.5% (161)

The algorithm in [7]

Salience-based multifeature fusion

The algorithm proposed in the chapter

Salience-based multifeature fusion

93% (186) 100% (200)

and illumination conditions, whereas the algorithm probably cannot detect some number plates with incomplete or occluded characters. As shown in Fig. 4.21a and b, occlusion greatly changes the number and completeness of characters on the number plate, while these features are a design basis for most detection algorithms, which sets a stricter requirement for the robustness. However, retroreflection features are effectively quantified as significant brightness under the quantification and imaging conditions of retroreflectors. The feature is insensitive to the character incompletion and occlusion. Normal characters, including the normal part of incomplete characters, all have high brightness, so that the multifeature fusion algorithm can cope with the special situation, as illustrated in Figs. 4.21c and d. In summary, the algorithm proposed in the chapter achieves a success rate of 100% in test set 1.

References 1. Huang YF, Chen YY. Analysis of the current research status of regression reflective materials. New Chemi Mater. 1999;27(9):22–5. 2. Ouyang YD, Zhou XP, Zhou JP. Reflective properties of glass bead materials. J Shantou Univ. 2004;19(2):23–7. 3. Zhu DT, Yang X, Hu YH, et al. A rapid measurement device and method for retroreflection coefficient of traffic signs. ZL202010785873.3[P]. 2020-08-07. 4. Liu JS. Infrared physics. Beijing: Ordnance Industry Press; 1992. 5. Yang X, Lv DL, Hu YH, et al. A collision warning method for retroreflector laser detection. ZL201610380057.8[P]. 2016-06-01. 6. Yang X, Hu YH, Zhao NX, et al. An imaging method for reverse reflector detection. ZL201910472120.4[P]. 2019-05-31. 7. Chen ZX, Liu CY, Chang FL, et al. Automatic license-plate location and recognition based on feature salience. IEEE Trans Veh Technol. 2009;58(7):3781–5. 8. Sun JX. Modern pattern recognition. Changsha: National University of Defense Science and Technology Press; 2002. 9. Chen ZX. Research on target recognition methods based on feature saliency and their applications. Wuhan: Huazhong University of Science and Technology; 2007. 10. Chen J, Yang X, Qiao Y, et al. A detection method for traffic signs. ZL201410421328.0[P]. 2014-08-25. 11. Yang X, Zhu D T, Hu YH, et al. A non uniform light retroreflector detection method. ZL201910376845.3[P]. 2019-05-07.

References

183

12. Yang X, Ling YS, Li S, et al. Graying for images with color-discrete characteristic. Int J Light Electron Opt. 2011;122(18):1633–7. 13. Sunlight [EB/OL]. [2010-11-20]. http://zh.wikipedia.org/wiki/%E5%A4%AA%E9%99% BD%E5%85%89 14. Yang X, Zhu DT, Hu YH, et al. A detection method for active polarized light retroreflector. ZL201910376220.7[P]. 2019-05-07. 15. Anagnostopoulos CNE, Anagnostopoulos IE, Psporoulas ID, et al. A license plate-recognition algorithm for intelligent transportation system applications. IEEE Trans Intell Transp Syst. 2006;7(3):377–92. 16. Yang X. Self-adaptive model of texture-based target location for intelligent transportation system applications. Int J Light Electron Opt. 2013;124(19):3974–82.

Chapter 5

Passive Imaging Detection on Identity Attributes of Targets

Visible marks describing various meanings are present on many equipment and carrying platforms, such as alphanumeric numbers and patterns. They are important components of target carriers and attached to target carriers, belonging to typical target-attached attributes. Identifying these marks can not only assist target detection but also provide support for various detection activities including target identification, tracking, and comprehensive analysis. These marks are special graphic objects and have two characteristics compared with traditional objects: at first, they are all artificial targets formed by standard color matching, showing typical color-discrete characteristic; then, marks and patterns vary together with the motion state of carrying platforms, shooting angle, and segmentation error, and show large dynamic ranges under natural conditions of noises, scale, and affine transformation. Traditional mark identification generally involves four processes, namely image acquisition, image preprocessing, detection and segmentation, and identification. Combining with the above two characteristics of marks on targets, the chapter mainly focuses on last three processes. Section 5.1 introduces the graying of color-discrete characteristic. Combining with mark examples, the application of preprocessing methods including the contrast maximization, image enhancement, and color-edge extraction is elaborated and influences of the graying methods on image segmentation and target extraction are deeply analyzed. Section 5.2 expounds mark identification based on the improved scale-invariant feature transform (SIFT) and focuses on discussion of robustness of the algorithm in coping with adverse conditions including the complex background, illumination variation, affine transformation, and feature defects.

© National Defense Industry Press 2023 X. Yang and Y. Hu, Photoelectric Detection on Derived Attributes of Targets, https://doi.org/10.1007/978-981-99-4157-5_5

185

186

5 Passive Imaging Detection on Identity Attributes of Targets

5.1 Grayscale Processing of Color-Discrete Characteristic Grayscale of color images is the basis of the processing technology of gray images, while the contrast of target areas in gray images is a bottleneck for performance of the technology. Therefore, maximizing the contrast of target areas in the graying process is of important significance.

5.1.1 Analysis of Grayscale Methods At present, almost all visible-light acquisition systems work under passive imaging conditions. When acquiring images using imaging sensors such as CCD cameras, processing sources are generally color images. Compared with processing technologies of color images, those of gray images have started early and been applied widely, and existing mature mark identification methods almost all take gray images as research objects. At the same time, color images contain abundant color information, which not only causes a huge memory cost but also affects the execution speed of systems. Therefore, it is necessary to transform color images into gray ones after acquiring the images when using the mark identification methods based on gray images. In the RGB model, if R = G = B, then the color represents one gray level, in which the value of R = G = B is the gray level g. The process that transforms color images into gray images is called graying of color images. The traditional graying methods include the following three types: (1) The maximum-value method: a method that makes the gray level equal to the largest one among three components, R, G, and B, that is, g = max(R, G, B).

(5.1)

(2) Mean-value method: a method that allows the gray level identical to the mean value of three components, R, G, and B, that is, g = (R + G + B)/3.

(5.2)

(3) Weighted-mean method: a method that assigns different weights to R, G, and B according to the importance and other indices and makes the gray level identical to the weighted mean of their values, that is, g = (W R R + WG G + W B B)/3,

(5.3)

where W R , WG , and W B separately represent the weights of R, G, and B. The visual sense of human is very sensitive to colors and can distinguish about 3500 colors while can only differentiate about 20 Gy levels. It is traditionally considered that the graying process should conform to visual characteristics of human as

5.1 Grayscale Processing of Color-Discrete Characteristic

187

far as possible. Particularly, gray images need to meet the visual discriminability as far as possible to further reduce the visual distortion from color to gray images. From this point of view, the maximum-value and mean-value methods are likely to cause distortion of images in the graying process. For example, the red pixels (255, 0, 0), green pixels (0, 255, 0), and blue pixels (0, 0, 255) are likely to be unified into a gray level when using the two methods, thus causing large areas of distortion, which seriously interferes the visual effect. Research has found that human eyes are most sensitive to green, followed by red, and least sensitive to blue, so the use of a combination of empirical weights W R = 0.3 × 3, WG = 0.59 × 3, and W B = 0.11 × 3 can obtain gray images that more conform to visual characteristics, that is, g = 0.3R + 0.59G + 0.11B.

(5.4)

The above equation is the classical graying method, that is, weighted-mean method. At present, data sources in almost all gray image processing are obtained using the method, including the mark identification method based on gray images in the chapter. Starting from the sensitivity of human eyes to various light rays and the imaging mechanism of optical equipment, numerous scholars have used the weighted-mean method to attain gay images of low distortion. The weighted-mean method well adapts to visual characteristics and such gray images afford better visual comfort. However, only a few image processing tasks aim to cater to the visual effect of human. Instead, the purpose of most tasks is to realize automation and intelligence and to reduce and even avoid manual intervention, so that people can simply handle numerous complicated batch work, such as target detection, identification, and tracking, including the research object of the chapter, namely mark identification. Therefore, effective graying methods should satisfy the requirement of machine processing to the greatest extent, rather than visual characteristics of human. Presence of significant contrast between targets and background is not only a basis for processing gray images but also a determinant of the processing effect, so realizing contrast maximation in the graying process should be the research object of the graying method. The weighted-mean method only considers visual characteristics and focuses on the overall visual effect of images, while it ignores the core objective; it even reduces the contrast of targets and background in the graying process. Meanwhile, focusing on visual distortion in the non-target area does not have apparent practical significance. Therefore, exploring a graying method that has the function to maximize the contrast in the target area is an emphasis in mark identification.

188

5 Passive Imaging Detection on Identity Attributes of Targets

5.1.2 Grayscale Processing Principle of Color-Discrete Characteristic Color-discrete characteristic is closely related to the three-color imaging mechanism. The section defines color-discrete characteristic in the target area on the basis of the three-color imaging mechanism and introduces a graying method for color-discrete characteristic.

5.1.2.1

Three-Color Imaging Mechanism

The retina of human eyes is covered by photoreceptor cells, which are similar to reception bases (pixels) on CCD chips, as illustrated in Fig. 5.1. The photoreceptor cells absorb light from optical images and focus light onto the retina via the lenticular lens and cornea. They emit nerve impulses, which are then transmitted to the brain through about one million of optic nerve fibers to finally form images. The photoreceptor cells in human eyes can be divided into two types: rods and cones. Therein, rods are sensitive and provide monochrome night vision with strong photosensitivity; cones enable visual perception of colors under high optical brightness. Cones are in three forms, which are classified according to their photochemical properties that transform optical signals to nerve impulses. Cones divide visible light into three bands, namely red, green, and blue ones, which are called the three primary colors of visual sense of human. Figure 5.2 displays the light sensitive curves of the three types of cones in the human visual system [1]. Considering characteristics of the human visual system, numerous manpower and material resources have been invested to the three-color system to perform electronic imaging. The three-color imaging mechanism of important significance is the design basis for visible-light imaging devices and it divides the visible spectrum into three bands (red, blue, and green ones) according to the spectral quantization characteristics of human eyes. Three-color images are formed in the three bands and they Fig. 5.1 Human eye structure

5.1 Grayscale Processing of Color-Discrete Characteristic

189

Fig. 5.2 Light sensitive curves of cones in human eyes

are superposed to represent color images. Such superposition produces an approximate effect of the real scenes, so it seems normal to human eyes. Existing true-color imaging is almost all completed using a three-color imaging device. The commonly used color cameras, digital cameras, video cameras, televisions, and CCD cameras are all typical three-color imaging devices. These imaging devices share approximate spectral response curves that are all similar to the sensitive curves of photoreceptor cells in human eyes.

5.1.2.2

Color-Discrete Characteristic

A kind of research object with cooperative features is widely present in fields including computer vision and pattern recognition that are closely related to image processing. They are color images showing color-discrete characteristic in targets and local background, such as visible marks on weapon platforms, small targets in the sky, ocean, and grassland, number plates and traffic signs and patterns, as displayed in Fig. 5.3. Local background is a concept relative to the background of an entire image. It is a non-target area that is closely related to a target. For example, in number-plate images, characters are the target, background is local background, while the car body beyond the number plate, road, and other objects are called collectively as global background. Color-discrete characteristic is described as the obvious color difference between the target and background, that is, a significant difference between the two in spectral reflection characteristics within the visible-light range. For each band of the three primary colors, such difference in spectral reflection characteristics tends to be maximum or minimum [2]. Figure 5.4 illustrates spectral reflection characteristics of a mark with white characters on blue background. The blue background materials have a peak spectral reflection characteristic in the central wavelength of blue light, which gradually decreases with the reduction or increment of wavelength, and the background exhibits very weak reflection in the red band. Materials of white characters have high spectral reflection characteristics in the whole visible-light range,

190

5 Passive Imaging Detection on Identity Attributes of Targets

(a) Weapon marks

(b) Small targets

(c) Traffic signs

(d) Number plates

Fig. 5.3 Color images with color-discrete characteristic

which also vary slowly. This reveals that the difference in spectral reflection characteristics of the two tends to be minimum in the blue band while tends to be maximum in the green and red bands. Similarly, the above weapon marks, small targets, and traffic signs and patterns also show the similar characteristics, through which the detailed color-discrete characteristic can be deduced, with no need to measure spectral reflection characteristics. For example, for yellow crosswalk signs, the spectral reflection characteristics of black characters are low in the whole visible-light range and change very slowly, while spectral reflection characteristics of yellow background have a peak near the central wavelength of yellow light, which decreases with the reduction or increase of wavelength. The central wavelength of yellow light is between those of green and red light, so that the difference in spectral reflection characteristics of the two tends to be minimum in the blue band while tends to be maximum in the green and red bands.

5.1 Grayscale Processing of Color-Discrete Characteristic

191

Fig. 5.4 Spectral reflection characteristics of marks with white characters on blue background

5.1.2.3

Graying Principle

According to the three-color imaging principle, color features of each pixel in color images are determined by four factors (spectral reflection characteristics of materials, spectral response functions of imaging devices, channel gain and photoelectric conversion coefficient of detectors) under certain illumination conditions (when ignoring light attenuation during transmission and attenuation caused by other optical devices). Among them, the channel gain and the photoelectric conversion coefficient of detectors are constants, given an imaging device. By quantizing reflection spectra of materials in the classified bands of three primary colors and comparing the three quantization results, the imaging device can synthesize the three primary colors into colors according to a certain rule. This corresponds to the familiar principle of color synthesis. For example, in the quantization results of imaging devices for yellow materials, the quantized values in red and green bands are approximate to each other and much higher than that in the blue band. Therefore, yellow can be synthesized by red and green under ideal conditions; in the corresponding RGB space, R, G, and B components of red and green pixels are (255, 0, 0) and (0, 255, 0), while those of yellow pixels are synthesized as (255, 255, 0) by the two. Similarly, pixels of other colors can be synthesized by R, G, and B components with different weights. The quantized feature functions for reflectance spectra of the target and local background are defined by Eq. (5.5). The functions can effectively describe color features of corresponding pixels of the target and local background. { ⎧ ⎪ , ⎪ f T (λ, λ ) = A RT (λ)TI (λ)dλ ⎪ ⎪ ⎪ ⎨ (λ,λ, ) { , ⎪ , ⎪ ⎪ f (λ, λ ) = A R (λ)T (λ)dλ L L I ⎪ ⎪ ⎩ ,

(5.5)

(λ,λ )

where A is the product of the channel gain and photoelectric conversion coefficient of detectors; RT (λ) and RL (λ) separately represent spectral reflectivity of targets and local background materials; TI (λ) is the spectral response function of the imaging

192

5 Passive Imaging Detection on Identity Attributes of Targets

device in the bands of three primary colors; I = {R, G, B} separately corresponds to bands of the three primary colors; (λ, λ, ) separately represents the ranges of three primary colors. Then, the quantized feature difference function D I (λ, λ, ) of the two is defined, as shown in Fig. 5.6. The function approximately describes the difference in color features of corresponding pixels of the target and local background, that is, approximately describes the difference in three-color images of the target and local background. | | D I (λ, λ, ) = | f T (λ, λ, ) − f L (λ, λ, )| = A

{ |RT (λ) − RL (λ)|TI (λ)dλ,

(5.6)

(λ,λ, )

where I has the same meaning as defined above. Dmax = max(D R (λ, λ, ), DG (λ, λ, ), D B (λ, λ, )).

(5.7)

It is deduced from Eq. (5.7) that the quantized feature difference corresponding to the target and local background is most obvious in the monochromatic band corresponding to Dmax . That is to say, in the monochromatic images corresponding to Dmax , the target and local background have the largest contrast. Under the condition, the primary color (one of red, green, and blue) corresponding to Dmax is defined as the grayscale color. In fact, the candidate primary color is determined at first if two of D R (λ, λ, ), DG (λ, λ, ), and D B (λ, λ, ) are approximate and apparently larger than the other one, or their values are all approximate. Then, the primary color with poor sensitivity of human eyes is then defined as the grayscale color (the three primary colors are listed as green, red, and blue in a descending order according to sensitivity of human eyes). For example, in the case of a white target and blue local background, D R (λ, λ, ) and DG (λ, λ, ) are approximate and obviously larger than D B (λ, λ, ), so red and green are candidate primary colors; then red, to which human eyes are less sensitive, is selected as the grayscale color. In practical application, an interested primary color can be directly selected to serve as the grayscale color. In fact, most cooperative targets have distinct colors and the grayscale color can be determined through visual observation and the color synthesis principle, without complex measurement and calculation, which further enhances practicability of the method. For color images in which the target and local background have color-discrete characteristic, the grayscale color can be used to directly gray corresponding components in the RGB space. The conversion equation is g(i, j ) = I (i, j ),

(5.8)

where g(i, j) is the gray level of a pixel (i, j ) in a gray image; I (i, j) is the R, G, and B components of the pixel (i, j ) in a color image corresponding to the grayscale color.

5.1 Grayscale Processing of Color-Discrete Characteristic

193

Taking four types of number plates (white characters on blue background, black characters on yellow background, white characters on black background, and black characters on white background) in China as examples, the following is judged using the graying method based on color-discrete characteristic: D R (λ, λ, ) and DG (λ, λ, ) are approximate and obviously larger than D B (λ, λ, ) in the green and red bands, so green and red are selected as candidate primary colors. Therefore, red is defined as the grayscale color. R component of various pixels in the color image is directly used for graying, that is, g(i, j) = R(i, j),

(5.9)

where R(i, j ) represents the R component of pixel (i, j) in the color image.

5.1.3 Preprocessing of Grayscale 5.1.3.1

Contrast Maximization

(1) Numerical analysis The contrast of the target area is represented as Cr = |gT − gL |/(gT + gL ),

(5.10)

ΣN Σ where gT = i=1 gi /N and gL = M j=1 g j /M separately represent average gray levels of the target and local background; gi and g j separately correspond the gray levels of two pixels; N and M are the numbers of two types of pixels; Cr is generally called the relative contrast. The weighted-mean method is adopted to attain the gray levels of pixels in the target and local background as {

gi = 0.3Ri + 0.59G i + 0.11Bi g j = 0.3R j + 0.59G j + 0.11B j

,

(5.11)

where (Ri , G i , Bi ) and (R j , G j , B j ) separately denote the R, G, and B components of the two types of pixels. The relative contrast obtained using the weighted-mean method is Cr

|Σ | ΣM | N | | i=1 (0.3Ri + 0.59G i + 0.11Bi )/N − j=1 (0.3R j + 0.59G j + 0.11B j )/M | = ΣN . ΣM i=1 (0.3Ri + 0.59G i + 0.11Bi )/N + j=1 (0.3R j + 0.59G j + 0.11B j )/M (5.12)

194

5 Passive Imaging Detection on Identity Attributes of Targets

The gray levels of pixels in the target and local background attained using the method based on color-discrete characteristic are { gi, = Ri . (5.13) g ,j = R j Correspondingly, the relative contrast is |Σ | ΣM | N | | i=1 Ri /N − j=1 R j /M | , Cr = Σ N . ΣM i=1 Ri /N + j=1 R j /M

(5.14)

The effects of the two graying methods under ideal and practical conditions can be discussed according to Eqs. (5.12) and (5.14). The number plates with white characters on blue background in the ideal case are taken for analysis. (Ri , G i , Bi ) and (R j , G j , B j ) are separately (255, 255, 255) and (0, 0, 255). Under the condition, Cr = 0.89; if the method based on color-discrete characteristic is adopted, Cr, = 1 is attained. This reveals that compared with the weighted-mean method, the method based on color-discrete characteristic retains the contrast between the target and local background in the graying process to the maximum extent, which provides high-quality input sources for subsequent image processing. Therefore, the method has high application value. In fact, the R, G, and B components of pixels in certain color are similar to those in the ideal case only in terms of the proportional relation due to factors including the color offset of materials themselves, illumination variation, and quantization error of imaging devices. Under the condition, it is particularly important to achieve contrast maximization in the target area. Figure 5.5 shows the graying effects of two methods on a number-plate image collected under weak illumination in the nighttime. It can be seen that the method based on color-discrete characteristic still has an obvious effect, which enables greater contrast in the target area than that using the weighted-mean method. It is calculated that Cr = 0.356 and Cr, = 0.498, which is consistent with the above conclusion. Therefore, the proposed method based on color-discrete characteristic achieves contrast maximization in the target area in the graying process, and it has significant advantages compared with the traditional weighted-mean method. The significance of contrast maximization lies in that it provides high-quality data sources for the image processing means including threshold segmentation and

(a) Color image

(b) Result of the weightedmean method

(c)Result of the method based on colordiscrete characteristic

Fig. 5.5 Graying effects of a number plate under weak illumination in the nighttime

5.1 Grayscale Processing of Color-Discrete Characteristic

(a) Color traffic signs

(b) Method based on color-discrete characteristic discrete characteristic and Otsu

(d) Weighted-mean method

195

(c) Method based on color-

(e) Weighted-mean method and Otsu

Fig. 5.6 Threshold segmentation effects of the Otsu method

even the whole image processing task, thus reducing the processing difficulty and improving the success rate. The influences of this characteristic of the method based on color-discrete characteristic on threshold segmentation and image processing tasks were discussed below by combining with practical applications. (2) Influence on threshold segmentation To meet the requirement of real-time performance, global threshold segmentation methods are widely applied to image processing tasks based on natural scenes, such as Otsu, entropy-maximum, and moment-preserving methods. The threshold selection of these methods is closely related to the gray-level distribution of all pixels, rather than being determined by local features. It requires the target area to have significant contrast, or threshold segmentation is very likely to fail. A total of 300 traffic-sign images in the natural scenes were collected as data sources. Then, these methods were adopted for threshold segmentation of images output by the method based on color-discrete characteristic and the weighted-mean method. The experimental results are shown in Table 5.1. Table 5.1 Experimental results of threshold segmentation Graying methods

Otsu (%)

Entropy-maximum (%)

Moment-preserving (%)

Weighted-mean method

79.0

75.7

74.3

Method based on color-discrete characteristic

94.3

92.7

92.0

196

5 Passive Imaging Detection on Identity Attributes of Targets

Corresponding to the three threshold segmentation methods, the method based on color-discrete characteristic achieves the highest success rate, which is obviously attributed to its characteristic of contrast maximization. Figure 5.6 shows results of the Otsu method in a group of samples. After graying, the images output by the method based on color-discrete characteristic have obviously higher contrast. Despite of a large dynamic range of global background, the Otsu method still yields successful segmentation results, as illustrated in Fig. 5.6b and c. However, targets are lost in images output by the weighted-mean method when using the same processing methods, as shown in Fig. 5.6d and e. The contrast maximization function is of positive practical significance for threshold segmentation, particularly for global threshold segmentation methods. (3) Influence on image processing tasks To evaluate influences of the method based on color-discrete characteristic on the specific image processing tasks, this method and the weighted-mean method were separately applied to the number-plate location algorithm proposed by Jiao et al. [3] and the traffic-sign location algorithm proposed by Chen et al. [4]. The data sources for tests are obtained using the following methods: at first, 100 natural scenes were selected, each of which contains a number plate; then, ten images were acquired from the same field of view (FOV) in each scene, in which nine images were collected separately using different filters while another one was collected normally (or regarded as being collected using a filter with transmittance of 100%). The transmittance of these filters varies from 90 to 10%, with an interval of 10%; to test the sensitivity of the two graying methods to illumination variation, the ten groups of images correspond to different transmittances and can be used as simulated images in the same scenes under different illumination conditions. Figure 5.7 shows ten sample images taken in a FOV. The same method was used to attain ten groups of traffic-sign images. If one of the candidate areas is a number plate or a traffic sign, the location is regarded successful. Figure 5.8 displays test results of combinations of two location algorithms and two graying methods, in which the abscissa represents the transmittance of filters corresponding to the current data set and the ordinate represents the success rate of location.

Fig. 5.7 Simulated sample images in the same FOV under illumination variation (the transmittance reduces successively from left to right)

5.1 Grayscale Processing of Color-Discrete Characteristic

197

Fig. 5.8 Test results of combinations of two location algorithms and two graying methods

Two important conclusions are reached from Fig. 5.8: one is that the success rates of the two location algorithms corresponding to the method based on color-discrete characteristic are obviously higher than those corresponding to the weighted-mean method; the other is that as the illumination weakens, the curves corresponding to the method based on color-discrete characteristic have a change rate smaller than those corresponding to the weighted-mean method. Therefore, the method based on colordiscrete characteristic is less sensitive to illumination variation than the weightedmean method. This indicates that the method based on color-discrete characteristic is more favorable for ensuring the success rate of image processing tasks.

5.1.3.2

Image Enhancement

Image enhancement methods can be divided into two types: global enhancement and local enhancement methods. For global enhancement methods, the gray level of each pixel in target images is only related to gray levels of all pixels in the original image, while not related to locations of these pixels. Typical representatives of these methods include contrast stretch and histogram equalization. Correspondingly, if the gray level of each pixel in the enhanced target image is only related to certain local features of the original image, this type of enhancement is called local enhancement methods, typical representatives of which include local standard deviation method and tophatbothat transform. These methods either rely on empirical parameters, have defects that are difficult to overcome, or have poor real-timelines and robustness, so that it is challenging for these methods to cope with the complex working environment

198

5 Passive Imaging Detection on Identity Attributes of Targets

for identifying various marks. Combining advantages of the method based on colordiscrete characteristic and the histogram equalization, a new image enhancement method, namely the image enhancement method based on color-discrete characteristic, is developed. The section pays attention to evaluation of enhancement effects of the image enhancement method based on color-discrete characteristic, contrast stretch, histogram equalization, local standard deviation method, and tophat-bothat transform from aspects of robustness, real-timeliness, and independence of empirical parameters. 1. Image Enhancement Methods (1) Contrast stretch. The essence of contrast stretch is to stretch the gray-level range of interest, so that bright pixels are brighter and dark ones are darker in the range, thus achieving the goal of enhancing the contrast. It is expressed as follows: ⎧ f 255 − n ⎩ ( f −n)×255 n ≤ f ≤ 255 − n 255−2×n where f is the gray level of pixels in the original image; g is the gray level of pixels in the target image. Calculation results expand the gray-level range from [n, 255 − n] to [0, 255]. (2) Histogram equalization. To change situations that an image is dark or bright on the whole or has insufficient gray levels, the histograms of original images can be corrected into uniform histograms through the transformation function, so that the histograms do not concentrate in the high or low end any longer but turn to uniform distribution. This technology is called histogram equalization and its realization method is expressed as {f g = T( f ) =

P f ( f )d f,

(5.16)

0

where the meanings of f and g are same as those in Eq. (5.15); T ( f ) is a transformation function; P f ( f ) is the probability density function of f . The right-hand side of Eq. (5.16) is the cumulative distribution function of P f ( f ), which means that when the transformation function is the cumulative distribution function of f , the goal of histogram equalization can be realized. For discrete digital images, frequency can be used to approximately replace probability. Supposing that the pixel number in an image is n, there are l gray levels, and n k represents the number of pixels at the gray level k, then the frequency for occurrence of the kth gray level is P f ( fk ) =

nk n

0 ≤ f k ≤ 1, k = 0, 1, . . . , l − 1.

(5.17)

5.1 Grayscale Processing of Color-Discrete Characteristic

199

The transformation function T ( f ) represented by Eq. (5.16) can be rewritten as gk = T ( f k ) =

k Σ

Pf ( f j ) =

j=0

k Σ nj j=0

n

,

(5.18)

where 0 ≤ f j ≤ 1 and k = 0, 1, . . . , l − 1. (3) Local standard deviation method. In the acquisition process of numberplate images, the number-plate area is likely to have low contrast due to shooting conditions of weak illumination and presence of shades. To solve the problem, Zheng et al. [5] proposed an image enhancement based on local standard deviation. They considered that the window scanning area is dark or bright when the local standard deviation is approximate to zero, under which enhancement is not necessary. At the same time, a large local standard deviation indicates great contrast in the window scanning area, under which enhancement is also not necessary. Thus, enhancement to different extents should be performed according to the local standard deviation. According to this, the enhancement operation can be expressed as Ii,j = f (σWi j ) · (Ii j − I Wi j ) + I Wi j ,

(5.19)

where Ii j and Ii,j separately represent gray levels of pixel Pi j in the original and target images; Wi j is the scanning window centered on the pixel Pi j ; I Wi j and σWi j separately denote the average gray level and standard deviation of all pixels in the corresponding window. Actually, to reduce computational cost, I Wi j and σWi j corresponding to some pixels are only calculated, while others are obtained through bilinear interpolation.

f (σWi j ) =

⎧ ⎪ ⎪ ⎨ ⎪ ⎪ ⎩

3 2 2 400 (σWi j −20) +1

3 2 2 1600 (σWi j −20) +1

1

0 ≤ σWi j < 20 20 ≤ σWi j < 60 .

(5.20)

σWi j ≥ 60.

f (σWi j ) is the enhancement factor defined by Eq. (5.20). Because σWi j in most number-plate areas that need to be enhanced is about 20, f (σWi j ) = 1 when σWi j = 0 or σWi j ≥ 60; when σWi j = 20, then f (σWi j ) = 3. Changes in the enhancement factor with the local standard deviation σWi j are shown in Fig. 5.9. (4) Tophat-bothat transform. The morphological algorithm of gray images includes four basic operations (corrosion, expansion, open operation, and closed operation). Assuming that the gray level of pixels in the original image I is f (i, j ) and that of pixels in the image B of structural elements is b(i, j ), then these operations are defined as follows:

200

5 Passive Imaging Detection on Identity Attributes of Targets

Fig. 5.9 Changes in the enhancement factor with the local standard deviation

Expansion: I ⊕ B = ( f ⊕ b)(i, j ) = max{ f (i − x, j − y) + b(x, y)|(i − x, j − y) ∈ D f , (x, y) ∈ Db },

(5.21)

where D f and Db separately represent the domains of definition of f (i, j ) and b(i, j ). Corrosion: I Θ B = ( f Θ b)(i, j ) = min{ f (i + x, j + y) − b(x, y)|(i + x, j + y) ∈ D f , (x, y) ∈ Db }.

(5.22)

Open operation: I ◦ B = I Θ B ⊕ B.

(5.23)

I • B = I ⊕ B Θ B.

(5.24)

Closed operation:

The following is obtained through open and closed operations: Tophat transform tophat(I ) = |I ◦ B − I |.

(5.25)

bophat(I ) = |I • B − I |.

(5.26)

Bothat transform

Tophat transform highlights pixels at high gray levels in the original images, while bothat transform shows pixels at low gray levels. Therefore, Hou et al. [6] designed a tophat-bothat transform method, which further

5.1 Grayscale Processing of Color-Discrete Characteristic

201

highlights areas with remarkable gray-level changes while inhibits areas with gentle gray-level changes, thus achieving the image enhancement function. The specific method is I , = tobhat(I ) + I − bothat(I ),

(5.27)

where I , is the enhanced target image. (5) Image enhancement method based on color-discrete characteristic. The graying method based on color-discrete characteristic maximizes the contrast of the target area in the graying process, so it can also be regarded as an image enhancement method, whereas the effect of the method is irrelevant to the gray level of pixels in the non-target area and not limited by the location and size of the target area, showing strong robustness. However, the method is unable to improve or lower the brightness of images. The enhancement effect of histogram equalization, as a global enhancement method, is easily affected by the non-target area, so the method exhibits poor robustness. Moreover, the method is also not limited by the location and size of the target area and also can improve or reduce the brightness of local areas in images. Hence, the method based on color-discrete characteristic and the histogram equalization are combined according to the strategy of compensating for each other to form a new enhancement method, namely the image enhancement method based on color-discrete characteristic [7]. 2. Performance Evaluation The robustness, real-timeliness, and independence of empirical parameter determine the performance of image enhancement methods. Among them, the independence of empirical parameter can be directly judged according to the enhancement principle. Here, two conditions, needing or not needing the empirical parameter, are set. The real-timeliness can be quantitatively described using the average execution time t. Because the image enhancement method based on color-discrete characteristic has the graying function, the time taken by the graying process of weighted-mean method is also considered when calculating the execution time of other methods. The robustness is quantitatively described from two aspects: contrast and deviation extent. Generally, the relative contrast in Eq. (5.10) is used to assess the enhancement effect. However, image segmentation and feature extraction in machine vision commonly directly harness the gray-level difference between the target and background, rather than the relative contrast. For example, the difficulty of segmenting two pixels at gray levels of 20 and 30 is same as that of segmenting two pixels at gray levels of 90 and 100 for some image segmentation methods. However, the relative contrast of the former data group is far larger than the latter group. In view of this, the absolute contrast is defined [8], namely Ca = |gT − gL |/255,

(5.28)

202

5 Passive Imaging Detection on Identity Attributes of Targets

where gT and gL separately denote the average gray levels of the target and local background. Simultaneously considering the relative contrast and absolute contrast is more favorable for effectively evaluating the enhancement effect. Therefore, the combined contrast is introduced, namely, C = (Cr + Ca )/2.

(5.29)

Apart from the expectation for greater contrast in the target area, the gray level of target pixels is also expected to have a smaller deviation extent. Meanwhile, the gray level of pixels in local background also needs to have a lower deviation extent. Only in this way can the integrity of the target and local background areas themselves be ensured, which provides favorable conditions for processes such as image segmentation and feature extraction. Therefore, a parameter for measuring the deviation extent is introduced, that is, the normalized mean squared error (NMSE). σT =

( N Σ

)1/ 2 ((gi − gT )/gT ) /N

,

(5.30)

⎞1/ 2 ) )2 g j − gL /gL /M ⎠ ,

(5.31)

2

i=1

and ⎛ σL = ⎝

M Σ (( j=1

where σT and σL denote NMSEs of the target and local background; N and M are the numbers of the two types of pixels, respectively. Similar to the concept of combined contrast, the NMSE is defined as σ = (σT + σL )/2.

(5.32)

A total of 20 number-plate images (768 × 576) with adverse factors such as insufficient illumination, color fading, and contamination were used to test the above image enhancement methods. Therein, the empirical parameter needs to be set in the local standard deviation method, tophat-bothat transform, and contrast stretch. According to the requirement of covering the whole image with an 8 × 8 window in previous research [5], the scanning window of the local standard deviation method is set to be 96 × 72. Because the empirical parameter of the other two methods is not set in relevant research, the structural element of tophat-bothat transform is set as the commonly used square structure with four sizes: 20 × 20, 40 × 40, 60 × 60, and 80 × 80; likewise, the parameter n of contrast stretch is set to four types: 20, 40, 60, and 80. Table 5.2 and Fig. 5.10 separately provide the evaluation experimental results and a group of sample images. Local standard deviation method and tophat-bothat

5.1 Grayscale Processing of Color-Discrete Characteristic

203

transform have poor real-timeliness, and their average execution time is separately more than 29 and 4 times of other methods. The image enhancement method based on color-discrete characteristic takes the shortest average execution time, which is only 37 ms, showing a significant advantage in real-timeliness. The image enhancement method based on color-discrete characteristic also has the optimal effect in robustness. The average combined contrast of the method is only slightly lower than that of gray stretch (n = 80), while the average combined NMSE is significantly lower. Meanwhile, compared with other methods, the average combined NMSE of the image enhancement method based on color-discrete characteristic is also only slightly higher than that of histogram equalization, while the average combined contrast of the former is significantly higher than that of histogram equalization. Therefore, the method not only effectively improves contrast of the target area but also controls the deviation extent of gray levels of the target and background as much as possible and renders the target area to be most significant, as shown in Fig. 5.10b. It is worth noting that the brightness of the first three and the fifth characters in the original image (Fig. 5.10a) is obviously lower than other characters due to contamination. However, the difference is significantly reduced by the image enhancement method based on color-discrete characteristic, which favorably balances the brightness of all characters, while the local standard deviation method, tophat-bothat transform, and contrast stretch cannot deal with the situation. Although histogram equalization can also improve this situation, Fig. 5.10c also shows that the method has combined contrast too low to be accepted. Besides, due to interpolation, the local standard deviation method also induces certain grayscale jump, as displayed in Fig. 5.10d. This inevitably brings a new difficulty for the subsequent image processing, which seriously interferes the processing effect. Therefore, the method based on color-discrete characteristic has stronger robustness than the other four methods. Different empirical parameters influence the real-timeliness and robustness of tophat-bothat transform. Its average execution time prolongs with the enlargement of the size of structural elements and at the same time, the different sizes all exert different enhancement effects. The setting of empirical parameter has even more significant influences on the enhancement effect of contrast stretch. Although the average combined contrast rises with the growing value of n, the average combined NMSE grows abruptly. This well describes why the contrast in the number-plate area is large, while the equalization of gray levels separately of characters and background reduces and even the gray levels of some pixels in characters and background become very low in Fig. 5.10l. Obviously, the image enhancement method based on colordiscrete characteristic that does not need to set the empirical parameter is more universal and of more obvious practical significance.

5.1.3.3

Color-Edge Extraction

Artificial marks and background have fixed color assortments that are different from a lot of background. Applying such fixed assortments as the constraint or

37

0.278

0.128

t/ms

Average C

Average σ

0.110

0.108

39 0.177

0.157

1306 0.205

0.204

177 0.262

0.262

179

0.295

0.246

185

0.250

0.228

198

44

43

44

80

0.145 0.193 0.289 0.452

0.103 0.128 0.191 0.285

42

Enhancement based on Histogram equalization Local standard Tophat-bothat transform Contrast stretch color-discrete deviation method 20 × 20 40 × 40 60 × 60 80 × 80 20 40 40 characteristic

Table 5.2 Experimental evaluation results of the five image enhancement methods

204 5 Passive Imaging Detection on Identity Attributes of Targets

5.1 Grayscale Processing of Color-Discrete Characteristic

(a) Original image

205

(b) Image enhancement method based on color-discrete characteristic Histogram equalization

(d) Local standard deviation method

(g) Tophat-bothat transform (60 × 60)

(j) Contrast stretch (40)

(e) Tophat-bothat transform (20 × 20) transform (40 × 40)

(h) Tophat-bothat transform (80 × 80)

(k) Contrast stretch (60)

(c)

(f) Tophat-bothat

(i) Contrast stretch (20)

(l) Contrast stretch (80)

Fig. 5.10 Samples in the enhancement experiments

directly extracting specific color edges is conducive to substantially suppressing noise textures, thus highlighting the target area. Research based on the idea generally determines what fixed colors that the current pixel belongs to at first, followed by searching for colors of corresponding assortments in the neighborhood of the pixel. Then, the color assortment of interest is determined through double color segmentation [8]. However, limited by sensitivity of color segmentation to adverse factors such as illumination variation and contamination and backward development of processing technologies for color images, these studies are basically still in the exploration stage [9]. Even if color spaces such as HIS and HSV are used to separate color information and brightness and powerful mathematical tools including neural networks,

206

5 Passive Imaging Detection on Identity Attributes of Targets

support vector machine, and fuzzy theory are employed, the effect still cannot be foundationally improved. In addition, influences of the complex feature extraction and tools themselves on real-timeliness also need to be considered comprehensively. Particularly, sensitivity of colors to illumination variation renders analysis methods relying on double color segmentation to have a low success rate and poor robustness. For the bule-white number plates, Liu et al. [10] described blue-white edges using the difference in RGB components corresponding to the two colors and only performed one time of color segmentation in the analysis process. After selecting an appropriate threshold, the color-edge extraction method can obtain a favorable effect and has enhanced stability under illumination variation. Besides, Chang et al. [11] also found that the color-difference analysis is conducive to improving the stability of color segmentation under illumination variation. In fact, the color-difference analysis is closely related to color-discrete characteristic, and its stability under illumination variation can be reasonably explained. Considering this, the section introduces an edge extraction method based on color-discrete characteristic [12] and expounds that the method hashigher robustness than traditional double color segmentation in coping with adverse conditions including illumination variation and contamination through comparative experiments. (1) Edge extraction method based on color-discrete characteristic The three quantized feature difference functions in Eq. (5.6) describe the difference between the target and local background in terms of color features in bands of the three primary colors. The three-color imaging mechanism quantizes color features of optical images into RGB components in CCD imaging devices, so the three functions and the corresponding (RTL , G TL , BTL ) can effectively represent color edges. Therein, RTL = |RT − RL |, G TL = |G T − G L |, and BTL = |BT − BL |; (RT , G T , BT ) and (RL , G L , BL ) separately represent RGB components of pixels in the target and local background. Number plates were taken as examples to design the edge extraction algorithm based on color-discrete characteristic. Number plates in 105 countries and regions were summarized [13], including 11 color assortments: cyan-black, cyan-white, black-red, black-white, blue-white, white-red, white-green, yellow-black, yellowblue, yellow-green, and yellow–red (yellow–red and red-yellow are regarded as one color assortment, which is similar in other cases). By using the above 11 color edges, the above color textures of number plates can be described. In the ideal case, the color assortments are divided into six groups according to the RGB components of color edges, as shown in Table 5.3. ( ) Taking blue-white color assortment as an example (Fig. 5.4), D R λ, λ, and ( ) ( ) DG λ, λ, are approximate and far larger than D B λ, λ, ; similarly, RTL and G TL are approximate and far larger than BTL . On the contrary, it is considered as bluewhite color assortment (needing to combine characteristics of blue and white). For example, (RT , G T , BT ) and (RL , G L , BL ) of blue-white assortment are (0, 0 , 255) and (255, 255 , 255). (RTL , G TL , BTL ) is (255, 255, 0). If the pixel color of an edge resembles yellow, it belongs to a blue-white or yellow-black edge. Therefore, the combination of these color edges can be segmented by relevant RGB components,

5.1 Grayscale Processing of Color-Discrete Characteristic

207

Table 5.3 Color assortments of number plates Groups

Color assortment

(RTL , G TL , BTL )

Hue H

Saturation S

1

Cyan-black and white-red

Cyan (0, 255, 255)

π

0

2

Black-red, cyan-white, and yellow-green

Red (255, 0, 0)

0

0

3

Black-white and yellow-blue White (255, 255, 255)

4

Blue-white and yellow-black Yellow (255, 255, 0)

5

White-green

Rosein (255, 0, 255)

6

Yellow–red

Green (0, 255, 0)

/ π 3 / 5π 3 / 2π 3

1 0 0 0

hue H, and saturation S of edge pixels. Therein, H and S are separately calculated using Eqs. (5.33)–(5.35). { H=

θ BTL ≤ G TL , 2π − θ BTL > G TL

(5.33)

3 [min(RTL , G TL , BTL )], (RTL + G TL + BTL ) ⎧ ⎫ ⎨ ⎬ (RTL − G TL ) + (RTL − BTL ) θ = arccos [ . ⎩ 2 (R − G )2 + (R − B )(G − B )] 21 ⎭ S =1−

TL

TL

TL

TL

TL

(5.34)

(5.35)

TL

Color edges in the natural scene are extracted using the following method: The first group: {

/ / (G TL − RTL ) G TL , (BTL − RTL ) BTL ≥ 0.5 / / . 5π 6 ≤ H ≤ 7π 6

(5.36)

The second group: {

/ / (RTL − G TL ) RTL , (RTL − BTL ) RTL ≥ 0.5 / / . 0 ≤ H ≤ π 6 or 11π 6 ≤ H ≤ 2π

(5.37)

The third group: {

The fourth group:

(RTL , G TL , BTL ) ≥ 60 . 0 ≤ S ≤ 0.2

(5.38)

208

5 Passive Imaging Detection on Identity Attributes of Targets

{

/ / (RTL − BTL ) RTL , (G TL − BTL ) G TL ≥ 0.5 / / . π 6≤H ≤π 2

(5.39)

The fifth group: {

/ / (RTL − G TL ) RTL , (BTL − G TL ) BTL ≥ 0.5 / / . 3π 2 ≤ H ≤ 11π 6

(5.40)

The sixth group: {

/ / (G TL − RTL ) G TL , (G TL − BTL ) G TL ≥ 0.5 / / . π 2 ≤ H ≤ 5π 6

(5.41)

Because number plates have ample vertical textures while car bodies have more significant transverse textures, color edges in the vertical direction are only extracted. Furthermore, considering the edge blur, the extraction is performed between pixels (i, j) and (i, j + 3). If the two pixels meet any one of the above six formulas and also satisfy some features of the two colors in the combination (e.g., RGB components of white pixels in the bule-white combination are all larger than those of blue pixels and B component of blue pixels is greater than the other two components), then I (i, j ) = 255; otherwise, I (i, j ) = 0. In fact, the color assortment belonging to the same group can also be subdivided according to some features of its own color. (2) Comparative experiments Taking number plates with white characters on blue background as examples, experiments were conducted to compare the traditional double color segmentation and the edge extraction method based on color-discrete characteristic. The former uses the parameter setting similar to the edge extraction method based on color-discrete characteristic. The method judges whether pixel (i, j ) belongs to blue or white at first and then judges whether pixel (i, j + 3) is the corresponding color or not. If they are both established, then I (i, j ) = 255; otherwise, I (i, j ) = 0. White and blue pixels are separately judged using Eqs. (5.38) and (5.42) (similar to judgment of green pixels in Eq. (5.41)). {

/ / (BTL − RTL ) BTL , (BTL − G TL ) BTL ≥ 0.5 / / . 7π 6 ≤ H ≤ 3π 2

(5.42)

Figures 5.11 and 5.12 separately show the extraction effects of the two methods for blue-white edges from overexposed and underexposed images. Severe edge losses are incurred by the double color segmentation method, while the edge extraction method based on color-discrete characteristic achieves a better effect. This is mainly because the adverse imaging condition causes distortion of blue and white to different extents, so that accurate results cannot be

5.1 Grayscale Processing of Color-Discrete Characteristic

(a) Original image

(b) Edge extraction method based on color-discrete characteristic

209

(c) Double color segmentation

Fig. 5.11 Color-edge extraction from an overexposed image

(a) Original image

(b) Edge extraction method based on color-discrete characteristic

(c) Double color segmentation

Fig. 5.12 Color-edge extraction from an underexposed image

obtained when judging the two colors separately; while the difference in the two colors remains good color-discrete characteristic, which enables one to obtain complete color edges using the method. For example, pixels of a group of bluewhite edges in the overexposed image are (RL , G L , BL ) = (166, 196, 236) and (RT , G T , BT ) = (250, 252, 255). The overexposure significantly enlarges RL and G L of blue pixels, so that Eq. (5.42) fails to make correct judgment. Under the condition, (RTL , G TL , BTL ) = (84, 56, 19) of the two pixels meets Eq. (5.39), which allows correct edge extraction. In the underexposed image, pixels of blue-white edges are (RL , G L , BL ) = (36, 50, 72) and (RT , G T , BT ) = (90, 88, 87); underexposure significantly lowers B L of blue pixels, such that Eq. (5.42) cannot make correct judgment. Under the condition, (RTL , G TL , BTL ) = (54, 38, 15) of the two pixels meets Eq. (5.39), which enables correct edge extraction. The situation with contamination can also be explained using the same cause. These experimental results show that the edge extraction method based on color-discrete characteristic has strong robustness in dealing with adverse factors such as illumination variation and contamination.

210

5 Passive Imaging Detection on Identity Attributes of Targets

5.2 Identification and Recognition Based on the Improved SIFT 5.2.1 Overview of the SIFT Operator The pattern recognition process aims to obtain matching features that always remain invariant under image variation factors including scaling, affine transformation, angle-of-view change, and illumination variation. Correspondingly, the operator for extracting these features is called an invariant. Meanwhile, matching features are also expected to have strong robustness to adverse factors including the complex background, contamination, and partial occlusion. The SIFT operator is a description operator for local features of images that remains stable under image scaling, affine transformation, and illumination variation proposed by David G. Lowe [14] on the basis of summarizing feature extraction methods based on the invariant. SIFT eigenvectors have the following advantages: (1) SIFT features are local features of images that remain invariant under rotation and scaling, and they have good stability under angle-of-view change, illumination variation, noise, and partial occlusion. (2) They have favorable independence and ample information and are suitable for rapid and accurate matching in the database containing massive features. (3) They show multiplicity. Lots of SIFT eigenvectors can also be produced even if there are a few objects. 5.2.1.1

Extremum Detection in the Scale Space

The scale-space theory first emerged in the computer vision field. It aims to simulate the multiscale features of image data. Koenderink [15] proved that the Gaussian convolution kernel is the only transform kernel for achieving scale transform. Lindeberg et al. [16] further verified that the Gaussian kernel is the only linear kernel. The two-dimensional (2D) Gaussian function is defined as: G(x, y, σ ) =

1 −(x 2 +y 2 )/ 2σ 2 e , 2πσ 2

(5.43)

where σ 2 is the variance of Gaussian normal distribution. In a 2D image, the scale space at different scales can be attained by images and convolution with a Gaussian kernel. L(x, y, σ ) = G(x, y, σ ) ⊗ I (x, y),

(5.44)

where L is the scale space of images; (x, y) is the pixel location of images; σ is the scale-space factor. The smaller the scale-space factor is, the less the image is smoothed and corresponding the smaller the corresponding scale. The large scale

5.2 Identification and Recognition Based on the Improved SIFT

211

corresponds to the general feature of images while the small scale corresponds to the detailed feature of images. In the 2D plane space of images and the difference of Gaussians (DOG) scale space, the local extremum is detected as the feature point at the same time, so that the feature has favorable uniqueness and stability. The DOG operator is defined as the difference between two Gaussian kernels at the same scale, which is characterized by simple calculation. It is the approximation of the normalized Laplacian of Gaussian (LOG) operator and expressed by D(x, y, σ ) = (G(x, y, kσ ) − G(x, y, σ )) ⊗ I (x, y) = L(x, y, kσ ) − L(x, y, σ ). (5.45) For points on the image, their responses to the DOG operator at each scale are calculated. By connecting these values, the feature-scale trajectory curves are obtained, local extremum points on which are the scale of the feature. It is entirely possible to have multiple local extremum points on the feature-scale trajectory curves. Under the condition, the point is regarded to have multiple feature scales. 2. Generation of Eigenvectors Generation of SIFT eigenvectors includes four steps: (1) Extremum detection in the scale space to preliminarily determine the location and scale of keypoints. When detecting extremums in the scale space, the pixel marked by × in Fig. 5.13 needs to compare with 26 pixels, including eight neighborhoods at the same scale, corresponding pixels at the adjacent scales, and eight neighborhoods of these pixels. This ensures that local extremums are detected in both the scale space and the 2D image space. (2) The locations and scales of keypoints are determined precisely by fitting the three-dimensional (3D) quadratic function. Meanwhile, unstable points are removed from keypoints, so as to enhance the stability and anti-noise property of matching. Fig. 5.13 Local extremums in the scale space

212

5 Passive Imaging Detection on Identity Attributes of Targets

At first, arbitrary components with offset and points whose contrast does not meet the threshold conditions are eliminated using the following equations: xˆ = −

( ) 1 ∂ DT ∂ 2 D −1 ∂ D , D x, ˆ x ˆ = D + ∂x2 ∂x 2 ∂x

(5.46)

where D represents a DOG image. Then, edge response points whose principal curvature does not meet the following conditions are eliminated: [ H=

] Dx x Dx y , D yx D yy

Tr(H) (r + 1)2 < , Det(H) r

(5.47)

where H is the Hessian matrix. (3) The orientation parameter of each keypoint is designated according to distribution characteristics of gradient orientation of pixels in the neighborhoods of keypoints, so that the operator has rotation invariance. At pixel (x, y), the gradient module m(x, y) and gradient orientation θ (x, y) are m(x, y) =

/ (L(x + 1, y) − L(x − 1, y))2 + (L(x, y + 1) − L(x, y − 1))2 , (5.48)

θ (x, y)

( / ) = arctan (L(x, y + 1) − L(x, y − 1)) (L(x + 1, y) − L(x − 1, y)) , (5.49)

where the scale of L is the scale of each keypoint. In practical calculation, sampling is conducted in the neighborhood window centered on the keypoint, and histograms are used for statistics of gradient orientations of pixels in neighborhoods. The histogram of oriented gradient (HOG) is in the range of 0–360° and totally contains 36 columns, with an interval of 10°. The peak in the histogram represents the major gradient orientation of the neighborhood at the keypoint, namely the orientation of the keypoint. Figure 5.14 is an example of determining the major orientation of a keypoint in the HOG of seven columns. In the HOG, if there is another peak, the energy of which is equivalent to 80% that of the main peak, the orientation is regarded as the auxiliary orientation of the keypoint. Multiple orientations (one major orientation and multiple auxiliary orientations) can be assigned to one keypoint, which can enhance the robustness of matching. In this way, each keypoint includes three types of information, namely the location, scale, and orientation, and all keypoints constitute a SIFT feature area. (4) SIFT eigenvectors are generated. At first, the coordinate axis is rotated to the orientation of the keypoint to ensure the rotation invariance. Then, a 8

5.2 Identification and Recognition Based on the Improved SIFT

213

Fig. 5.14 Determination of the major orientation in the HOG

× 8 window is selected taking the keypoint as the center. The center of the left panel in Fig. 5.15 is the location of the current keypoint, and each grid represents a pixel in the scale space of the neighborhood of keypoint. The direction and length of arrows separately denote the gradient orientation of the pixel and the value of gradient module. The circle in Fig. 5.15 indicates the range of Gaussian weighting (pixels closer to the keypoint contribute greater to the information of gradient orientation). The HOG of eight orientations is calculated in each 4 × 4 block, and the accumulated values of each gradient orientation are drawn, thus forming a seed point, as displayed in the right panel of Fig. 5.15. In the figure, a keypoint is composed of 2 × 2 (4) seed points, each of which has information of eight orientation vectors. The idea of combining orientation information in the neighborhood enhances the anti-noise property of the algorithm and at the same time provides favorable fault tolerance for feature matching with a location error. In practical applications, to strengthen the stability of matching, Lowe suggested to describe each keypoint using 4 × 4 (16) seed points. In this way, 128 data can be generated for one keypoint, thus forming the 128-dimensional SIFT eigenvectors. Under the condition, SIFT eigenvectors have removed influences of geometric transformation

214

5 Passive Imaging Detection on Identity Attributes of Targets

Fig. 5.15 32-dimensional vector representation of the feature points

factors including scale variation and rotation, and the influence of illumination variation can be further removed by normalizing the length of the eigenvectors.

5.2.2 Elliptical-Neighborhood SIFT Operator 5.2.2.1

Construction Principle

For each feature point, SIFT can be used to calculate the major gradient orientation in a circular neighborhood, the size of which is determined by the scale of the feature point. However, the invariance under affine transformation cannot be well described using the circular neighborhood. For example, the image structure in the circular neighborhood after affine transformation is very likely to turn to an elliptical area, as illustrated in Fig. 5.16a and c. If the circular neighborhood is still used, the image structure after transformation includes not only the required area but also noise areas that impair invariance computation, as shown in Fig. 5.16b. Under the condition, the two circular neighborhoods before and after transformation have different image structures, for which the circular neighborhood cannot obtain a stable major gradient orientation. Additionally, the image structure obtained using the elliptical neighborhood is very approximate to the real situation and is more stable when calculating the major gradient orientation. Therefore, the method based on an elliptical neighborhood proposed by Li et al. was used to improve the SIFT operator, which was then applied to mark identification [17]. The second-moment matrix is commonly used to describe local image structures. Describing the gradient distribution in local neighborhoods of point x using the second-moment matrix of gradient magnitude can effectively determine the shape of neighborhoods. Therefore, the second-moment matrix of gradient magnitude can

5.2 Identification and Recognition Based on the Improved SIFT

215

(a) Image structure in the circular (b) Affine transformation (c) Elliptical neighborhood that neighborhood changes the image structure in does not change the image the circular neighborhood structure after affine transformation

Fig. 5.16 Circular and elliptical neighborhoods

be used to estimate the elliptical neighborhood of each feature point. Considering the invariance under affine transformation, the circular neighborhood needs to be replaced with the elliptical neighborhood in the affine Gaussian scale space to calculate the second moment. The affine Gaussian scale space can be obtained through convolution with a non-uniform Gaussian kernel: g(x, Σ) =

( / ) 1 exp −x T Σ −1 x 2 , √ 2π det Σ

(5.50)

where x ∈ R 2 ; Σ is the symmetric semi-positive definite covariance matrix of the corresponding scale. If the matrix Σ is equal to the product of the unit matrix and a scaler, the equation is converted into the uniform Gaussian kernel: The affine Gaussian space of intensity image I (x) is expressed by L(x, Σ) = g(x, Σ) ∗ I (x),

(5.51)

where ∗ represents the convolution operator of images. The second-moment matrix μ of feature point x in the non-uniform Gaussian space is defined as ( μ(x, Σ I , Σ D ) =

μ11 μ12 μ21 μ22

)

) ( = g(x, ΣI ) ∗ ∇ L(x, Σ D )∇ L(x, Σ D )T ,

(5.52)

where Σ I ∈ SPSD(2) is the covariance matrix of the global scale σI ; Σ D ∈ S P S D(2) is the covariance matrix of the local scale σD ; ∇ is the gradient operator. ( ∇ L(x, Σ D ) =

) L x (x, Σ D ) , L y (x, Σ D )

(5.53)

where SPSD(2) denotes the cone of a 2 × 2 symmetric positive definite matrix.

216

5 Passive Imaging Detection on Identity Attributes of Targets

Lindeberg et al. [18] designed an iterative program to adaptively adjust the covariance matrix, so as to closely link features of a fixed point to the second moment in the equation below. If the second-moment matrix is calculated under the following conditions, it is invariant under arbitrary affine transformation. μ(x, Σ I , Σ D ) = M, Σ I = σI M −1 , Σ D = σD M −1 .

(5.54)

It is assumed that the determinant of the second-moment matrix is larger than zero and the signal-to-noise (SNR) ratio of target points is significant enough. By using the iterative program, the elliptical affine neighborhood of the target points can be evaluated. The method is adopted to estimate the shape of the initial elliptical affine neighborhood generated by the Hash operator. The eigenvalue of the second-moment matrix represents two major signal changes in the neighborhood of target points and is used to measure the shape of the elliptical affine neighborhood. To guarantee that sample points in the elliptical neighborhood of feature points have an appropriate size, the elliptical neighborhood is standardized into a circle using the elliptical parameter produced by the second-moment matrix of target points. By using the root mean square of the second moment M −1/ 2 , the image data can be converted into standard structures. The matrix can be attained through Cholesky decomposition, and the location x of each sample point in the elliptical area can be transformed into the location x , in the standard circular area. x , =M −1/ 2 .

(5.55)

Based on the standardized circular neighborhood, the major orientation of SIFT feature points can be set to guarantee invariance under affine transformation. Under the condition, the Gaussian-smoothed image L with a scale most approximate to the local scale σD is attained through convolution with the uniform Gaussian kernel. All calculation in the smoothed image is performed in the scale-invariant mode. Each pixel x , (x, y), gradient module x , (x, y), and gradient orientation θ (x, y) in the standardized circular neighborhood are calculated in advance using the pixelvalue difference as follows: / ( )2 ( )2 L(x + 1, y) − L(x − 1, y) + L(x, y + 1) − L(x, y − 1) , m(x, y) = (5.56) (( )/ ( )) θ (x, y) = arctan L(x, y + 1) − L(x, y − 1) L(x + 1, y) − L(x − 1, y) . (5.57) Similar to the traditional SIFT operator, the gradient vectors of each pixel in the standard circular area are used to establish the HOG based on the gradient module and the uniform Gaussian weighting function. Therein, the orientation corresponding to the maximum histogram statistic is the major gradient orientation of feature points.

5.2 Identification and Recognition Based on the Improved SIFT

217

The elliptical-neighborhood SIFT operator improved on this basis can obtain more stable invariance under affine transformation.

5.2.2.2

Performance Comparison

The improvement effect of the elliptical-neighborhood SIFT operator was tested by designing a group of comparative experiments. At first, three groups of experimental images including the digital, letter, and Chinese character marks were collected, as shown in Fig. 5.17. Observation of Fig. 5.17 reveals the following characteristics: ➀ each group contains eight images, which are all acquired from the same target under the same weather condition and numbered as 0# to 7#; ➁ the 0# image basically does not have 3D distortion, while the 3D distortion of 1# to 7# images gradually enlarges; ➂ the resolution of 0# to 7# images decreases successively, which avoids generation of new feature points due to the improved resolution and ensures gradual reduction of the overall quality of images; therein, the resolution of the three 0# images is separately (300 × 290), (300 × 300), and (600 × 360); ➃ 3D distortion includes two cases, affine transformation and blur. Feature points in three groups of images are extracted separately using the circular- and elliptical-neighborhood SIFT operators. The two types of SIFT feature points of the three 0# images are taken as the templates to match features in other images in the same group using the specific matching method in the common matching strategy in Sect. 5.2.3. Finally, the statistical results for the numbers of matching points in the three groups of images using the circular- and elliptical-neighborhood SIFT operators are obtained, as shown in Figs. 5.18, 5.19 and 5.20. It can be found from the three groups of statistical images that for each distorted image, the number of matching points obtained using the elliptical-neighborhood SIFT operator is always larger than that attained using the circular-neighborhood

Fig. 5.17 Experimental images for the comparative experiments

218

5 Passive Imaging Detection on Identity Attributes of Targets

Fig. 5.18 Comparison of the numbers of matching points in the digital-mark image using the circular- and elliptical-neighborhood SIFT operators

Fig. 5.19 Comparison of the numbers of matching points in the letter-mark image using the circularand elliptical-neighborhood SIFT operators

Fig. 5.20 Comparison of the numbers of matching points in the image of Chinese character marks using the circular- and elliptical-neighborhood SIFT operators

5.2 Identification and Recognition Based on the Improved SIFT

219

SIFT operator. The result effectively verifies the robustness of the ellipticalneighborhood SIFT operator in dealing with the 3D distortion, allowing the theory and practice to corroborate each other. Additionally, as the 3D distortion gradually enlarges, the difference in the numbers of matching points when using the two algorithms shows the characteristic of gradual increase to the peak at first and then decrease. This indicates that within a certain range of distortion, the invariance under affine transformation of the elliptical-neighborhood SIFT operator is greatly improved compared with that of the circular-neighborhood SIFT operator and the extracted feature points are more stable and discriminable. This further verifies the reasonability and effectiveness of the improved SIFT operator from another aspect. Certainly, as the 3D distortion is further deteriorated, the advantage of the elliptical-neighborhood SIFT operator relative to that of the circular-neighborhood one gradually weakens, which is inevitable. In fact, the two operators both can obtain sufficient feature points in images with slight distortion, and the only difference lies in the different sensitivities of the stability of these feature points to the distortion. Figure 5.21 displays feature-point extraction from images of 0# to 7# marks using the two operators, which preliminarily explains the different sensitivities. Almost the same numbers of feature points are extracted from 0# image using the two operators at first, while the number difference is observed in feature-point extraction from 7# image. In fact, even if feature points are extracted at the same location, they still differ in the stability, which also suggests that the number difference of matching points obtained using the two operators is finally larger than the number difference of feature points extracted.

5.2.3 Identification Methods For a specific mark image, SIFT feature points differ in the stability. It is feature points with high repeatability and resolution that are needed to serve as the matching template. Therefore, it is necessary to perform clustering analysis on SIFT feature points of marks. Here, the clustering process is introduced by taking Chinese characters on number plates as examples [19]. Each feature point is composed of four factors, namely the 128-dimensional descriptor, orientation, scale, and location, as displayed in Fig. 5.22, in which the mark of each SIFT feature point is V (des, ori, rat, pos). Therein, des is the 128dimensional SIFT descriptor; ori is the major gradient orientation (−π ≤ ori < π); rat = h/s (s is scale); pos = ( f /W, e/h), which represents the 2D vector at the corresponding location of the feature point. For each Chinese character on the number plate, 100 images were collected under the natural condition to serve as the training samples. To retain as many representative feature points as possible for each character, these images are placed vertically, with small affine deformation, almost no contamination, and uniform illumination. All SIFT feature points of each character are extracted, including some noise points, followed by feature clustering using the

220

5 Passive Imaging Detection on Identity Attributes of Targets

Fig. 5.21 Examples of feature-point extraction using circular- and elliptical-neighborhood SIFT operators (left: circular-neighborhood SIFT operator, right: elliptical-neighborhood SIFT operator, 0# to 7# marks) Fig. 5.22 Schematic diagram for feature points of a sample mark

similarity transfer method [20]. The most prominent characteristic of the method is that it does not need to determine the number of clusters in advance, which can be adaptively generated in the clustering process. The similarity matrix of samples needs to be defined in the similarity transfer process. At first, the distance measure-based rule needed by the similarity matrix should be defined at first. The distance between V j and V k of two sample features is defined as

5.2 Identification and Recognition Based on the Improved SIFT

d j,k = α · Dd + β · Do + γ · Dr + δ · Dp ,

221

(5.58)

where α, β, γ , and δ are constant weight factors; Dd , Do , Dr , and Dp separately represent distances of the SIFT descriptor, orientation, aspect ratio, and location, which are defined as follows: [ | 128 ( )2 Σ 1| j desi − desik , Dd = | (5.59) σ i=1 Do =

| | |) (| 1 · min |ori j − orik |, 2π − |ori j − orik | , π

(5.60)

| 1 || j rat − ratk |, N

(5.61)

Dr =

[ | 2 ( )2 Σ 1| j posi − posik , Dd = | 2 i=1

(5.62)

where σ and N are normalization factors to guarantee that Dd and Dr are valued in the range of (0, 1). The distance similarity between V j and V k of two sample features is ( )n s j,k = − d j,k

n > 0.

(5.63)

During similarity transfer, diagonal elements in the similarity matrix are selective for samples, which affects the number of clusters. Generally, no priori values are needed, and the diagonal elements are set using the mean value of two input similarities. After clustering, more representative feature points need to be selected and saved. The number of images corresponding to features that are classified into one cluster is counted to draw the histogram. A threshold 60 is set to screen feature points. Sample points with the image number larger than or equal to the threshold are retained, as shown in Fig. 5.23. In this way, the template for SIFT feature points of Chinese characters is formed, as displayed in Fig. 5.24. Assuming that Di,min 1 and Di,min 2 are the first and second nearest distances, if 2 2 Di,min 1 < σ Di,min 2 , 0 < σ < 1, then the two feature points can be regarded as a pair of stable matched feature points [21]. Feature matching is generally performed on all points between the identification object and the template to finally determine the identification results. Figure 5.25 shows an example of the matching strategy. However, numerous experimental data have shown that the matching strategy is less efficient and the identification results can be determined as long as there are three stable matched feature pointes. Therefore, a three-point center matching strategy was

222

5 Passive Imaging Detection on Identity Attributes of Targets

Fig. 5.23 Clustering of feature points Fig. 5.24 Template for SIFT feature points

5.2 Identification and Recognition Based on the Improved SIFT

223

Fig. 5.25 Identification example of all matched feature points

designed, as displayed in Fig. 5.26. Objects to be identified are divided into four parts by taking the geometric centers as boundaries. As long as feature points are found in each part, matching is executed immediately to match them from part 1 to part 4. As long as three groups of matched feature points are determined, the feature extraction and matching process is ended and identification results are output. Moreover, if at least three matched feature points cannot be obtained, the object is regarded as a noise area. The scanning mode is shown as the arrow in Fig. 5.26. Figure 5.27 illustrates the identification example of the three-point center matching strategy. The algorithm of the strategy is designed as follows: (1) Area 1 is scanned from right to left and from bottom to top. If one or no stable matching point is found, the matching continues. (2) Area 2 is scanned from left to right and from bottom to top. If one or no stable matching point is found, the matching continues. (3) Area 3 is scanned from top to bottom and from right to left. If one or no stable matching point is found, the matching continues. (4) It turns to step (10) if three groups of stable matching points are obtained; otherwise, the matching continues. (5) Area 4 is scanned from top to bottom and from left to right. If one or no stable matching point is found, the matching continues.

224

5 Passive Imaging Detection on Identity Attributes of Targets

Fig. 5.26 Three-point center matching strategy

Fig. 5.27 Identification examples of the three-point center matching strategy

(6) It turns to step (10) if three groups of stable matching points are obtained; otherwise, the matching continues. (7) It turns to step (9) if no stable matching points are attained; otherwise, the matching continues.

5.2 Identification and Recognition Based on the Improved SIFT

225

(8) Areas from which stable matching points are obtained are scanned continuously. It turns to step (10) if finding the third group of stable matching points; otherwise, the matching continues. (9) The objects to be identified are a noise area and the results are output. (10) The objects to be identified are a target area and the results are output.

5.2.4 Experimental Tests 5.2.4.1

Effect Comparison

To evaluate the identification methods, target images containing 885 Chinese characters were acquired. These number plates with Chinese characters were extracted using the location method proposed in previous research [7], including 754 real number plates and 131 noise areas. Chinese characters were obtained through artificial segmentation, with the size between 30 × 54 and 112 × 200. A target image contains one Chinese character, most of which shows inclination, complex noises, and non-uniform illumination to different extents and a few characters also exhibit contamination, blur, partial occlusion, and incompleteness. The success rates for identifying Chinese characters and noises are 97.2% and 100%, and the total success rate reaches 97.2%. A total of 21 identification failures are caused by adverse situations such as contamination and blur. To assess the real-timeliness of the three-point center matching strategy, the common all-point matching strategy and the common three-point matching strategy were used to carry out identification experiments in the same data set. The results are listed in Table 5.4. Obviously, the three strategies show the same success rate because the feature matching results are irrelevant to the scanning process, while the three strategies differ greatly in the execution efficiency. The average execution time of the three-point center matching strategy is only 68 ms (PC: Pentium IV at 2.4 GHz and 1 GB RAM; Tool: VC++ 6.0), which is obviously shorter than the other two. The difference occurs because (1): the extraction and matching of SIFT feature points are time consuming and the common all-point matching strategy has the lowest efficiency with the average execution time of 245 ms. (2) The common three-point matching strategy needs to extract all feature points while only requires three stable matching points, the execution time of which is 87 ms shorter than the former (common all-point matching strategy). In fact, the three-point center matching strategy only needs to extract fewer feature points and at the same time only requires three stable matching points, thus having the optimal efficiency.

226

5 Passive Imaging Detection on Identity Attributes of Targets

Table 5.4 Comparison of experimental results Common all-point matching strategy

Common three-point matching strategy

Three-point center matching strategy

Success rate (%)

97.6

97.6

97.6

Average execution time (ms)

245

158

69

5.2.4.2

Example Analysis

To analyze influences of affine transformation on identification performance, the following comparative experiments were designed: 100 successfully identified Chinese characters were selected to obtain four groups of data through four affine transformations, as displayed in Fig. 5.28. Samples in Fig. 5.28 were obtained according to the following steps: ➀ each image is compressed horizontally at a step of 6.25% to form 10 candidate objects and obtain

(a) Horizontal compression

(b) Vertical compression

(c) Clockwise rotation

(d) Horizontal compression and clockwise rotation Fig. 5.28 Affine-deformed samples

5.2 Identification and Recognition Based on the Improved SIFT

227

Fig. 5.29 Identification results under different affine deformations

10 groups of candidate objects according to different compression rates. Figure 5.28a displays a sample; ➁ each image is compressed vertically at a step of 6.25%; ➂ each image is rotated clockwise at a step of 5°; ➃ each image is compressed horizontally at a step of 6.25% and rotated clockwise at a step of 5° simultaneously. Similarly, the other three categories in Fig. 5.28b–d are obtained, each of which includes 10 groups of candidate objects. The above four categories of images were taken as objects to test the identification methods, thus attaining the change curves of success rates in Fig. 5.29. Obviously, the 2D rotation hardly has any influences on the identification. Even if the image is rotated by 50°, success identification still can be achieved and the whole identification rate reaches 100%. However, the identification process is very sensitive to the combination of horizontal compression and rotation, that is, 3D deformation. The success rate is only 55% when the compression rate is 62.5% and the rotation angle is 50°. Moreover, if the character width is smaller than the height, better identification results are more likely to be attained under horizontal compression than vertical compression at the same compression rate. Meanwhile, the first five groups of objects in each category all achieve success rates higher than 90%, which indicates that the identification process can still adapt to affine transformation to a certain extent. It is worth noting that very severe affine deformation rarely appears in practice. Due to local description characteristics and scale invariance of SIFT features, a satisfying identification effect still can be obtained under complex noises and largescale variation. When introducing lots of noises around characters, the identification effect is hardly affected, as illustrated in Fig. 5.30. Similarly, the effect in Fig. 5.31 is attained under large-scale variation. The time cost in the two cases increases significantly, which reduces the identification efficiency. Due to the local description characteristics of the SIFT operator, most incomplete characters and partial occlusion do not influence the identification effect. In this case, successful identification can be ensured as long as three groups of stable matching points can be obtained in some characters, as displayed in Figs. 5.32 and 5.33.

228

5 Passive Imaging Detection on Identity Attributes of Targets

(a) Slight noises

(b) Lots of noises

Fig. 5.30 Identification examples under background noises

(a) Small scale Fig. 5.31 Identification examples under scale variation

(b) Large scale

5.2 Identification and Recognition Based on the Improved SIFT

Fig. 5.32 Identification examples of incomplete characters

Fig. 5.33 Identification examples of partially occluded characters

229

230

5 Passive Imaging Detection on Identity Attributes of Targets

Fig. 5.34 Identification examples of contaminated characters

The identification method based on the improved SIFT remains good robustness to certain contamination and blur. If enough details are not contaminated and blurred, three groups of stable matching points still can be found, as illustrated in Figs. 5.34 and 5.35. In fact, identification cannot be realized in some severe cases, as shown in Fig. 5.36.

5.2 Identification and Recognition Based on the Improved SIFT

Fig. 5.35 Identification examples of blurred characters

Fig. 5.36 Identification examples of severely contaminated and blurred characters

231

232

5 Passive Imaging Detection on Identity Attributes of Targets

References 1. Wald G. The receptors for human color vision. Science. 1964;145(3636):1007–17. 2. Yang X, Ling YS, Li S, et al. Graying for images with color-discrete characteristic. Int J Light Electron Opt. 2011;122(18):1633–7. 3. Jiao JB, Ye QX, Huang QM. A configurable method for multi-style license plate recognition. Pattern Recogn. 2009;42(3):358–69. 4. Chen X, Yang J, Zhang J. Automatic detection and recognition of signs from natural scenes. IEEE Trans Image Process. 2004;13(1):87–99. 5. Zheng D, Zhao Y, Wang Y. An efficient method of license plate location. Pattern Recogn Lett. 2005;26(15):2431–8. 6. Hou PG, Zhao J, Liu M. A license plate locating method based on tophat-bothat changing and line scanning. J Phys: Conf Ser. 2006;48(1):431–6. 7. Yang X. Self-adaptive model of texture-based target location for intelligent transportation system applications. Int J Light Electron Opt. 2013;124(19):3974–82. 8. Yang X. Enhancement for road sign images and its performance evaluation. Int J Light Electron Opt. 2013;124(14):1957–60. 9. Liu WJ, Jiang QL, Zhang C. A license plate localization method based on CNN color image edge detection. J Autom. 2009;35(12):1503–12. 10. Liu SY, Li ZM. Research on license plate positioning technology under complex lighting conditions. J Electron Meas Instrum. 2005;19(6):93–7. 11. Chang SL, Chen LS, Chung YC. Automatic license plate recognition. IEEE Trans Intell Transp Syst. 2004;5(1):42–53. 12. Yang X, Hao XL, Zhao G. License plate location based on trichromatic imaging and colordiscrete characteristic. Int J Light Electron Opt. 2012;123(16):1411–504. 13. Coninx J. License Plate Mania [EB/OL]. http://licenseplatemania.com 14. Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 2004;60(2):91–110. 15. Koenderink JJ. The structure of images. Biol Cybern. 1984;50(5):363–70. 16. Lindeberg T. Seale-space theory: a basic tool for analyzing structures at different scales. Int J Appl Stat. 1994;21(2):224–70. 17. Li CL, Ma LZ. A new framework for feature descriptor based on SIFT. Pattern Recogn Lett. 2009;30(5):544–57. 18. Lindeberg T, Garding J. Shape-adapted smoothing in estimation of 3-D shape cues from affine deformations of local 2-D brightness structure. Image Vis Comput. 1997;15(6):415–34. 19. Chen HL, Hu B, Yang X, et al. Chinese character recognition for LPR application. Int J Light Electron Opt. 2014;125(9):5295–302. 20. Frey BJ, Dueck D. Clustering by passing messages between data points. Science. 2007;315(5814):972–6. 21. Wang Y, Ban XJ, Hu B, et al. License plate recognition based on SIFT feature. Int J Light Electron Opt. 2015;126(21):2895–901.

Chapter 6

Detection and Processing of Synthetic Attributes of Integrated Aerospace Images of Targets on the Ground and Sea Surface

Target detection in modern IT-based warfare is increasingly challenged by the large motion range, wide distribution range, and strong stealth capacity of targets. Particularly in the long-range precise strike, higher requirements have been raised for the finding, location, and identification of targets. More advanced imaging detection means have received more attention and fusion of multiple detection platforms and multiple detection data have become a development trend for target detection and information processing. To this end, airborne and spaceborne images are fused to generate new airborne/spaceborne integrated images considering the wide detection range and high efficiency of spaceborne platforms, complete detection data and high precision of airborne platforms, and lots of detection bands and high accuracy of hyperspectral imaging. Then, the airborne/spaceborne integrated images and the new attributes resulting from image fusion are detected and processed, so as to detect and identify targets, especially ground and sea-surface targets. The chapter mainly expounds the processing of synthetic attributes in airborne/spaceborne integrated images of ground and sea-surface targets and introduces core methods for processing synthetic attributes in detection images. The contents mainly include modeling and simulation for fusion of airborne/spaceborne integrated detection images, data optimization in detection images, and classification of synthetic attributes in images. In addition, a method specific for target detection and identification based on detection image fusion is introduced taking targets in the sea background as examples.

6.1 Modeling and Simulation of Detection Images Fusion Based on Integrated Aerospace Simulation for fusion of airborne/spaceborne integrated detection images refers to projective transformation of ground objects from airborne hyperspectral images to spaceborne multispectral images by combining spaceborne multispectral remotesensing parameters and based on airborne hyperspectral data. In this way, simulated © National Defense Industry Press 2023 X. Yang and Y. Hu, Photoelectric Detection on Derived Attributes of Targets, https://doi.org/10.1007/978-981-99-4157-5_6

233

234

6 Detection and Processing of Synthetic Attributes of Integrated …

spaceborne multispectral images of specific ground objects based on data fusion are generated. Starting from fusion and transformation of airborne/spaceborne integrated remote-sensing images, the section introduces how to carry out airborne/spaceborne fusion and simulation based on hyper/multispectral images by comprehensively considering relevant influencing factors of airborne and spaceborne remote-sensing imaging. As shown in Fig. 6.1, the feature models of ground objects and detector responses are separately packed in two black boxes in the modeling and simulation for fusion of airborne/spaceborne integrated detection images. Airborne hyperspectral images and spaceborne multispectral images easy to obtain are used to separately substitute the three-dimensional (3D) feature model of targets and the feature model of detector responses. This effectively reduces the tremendous workload needed for 3D modeling of ground objects and modeling of detector responses. The simulation method is also simple and feasible, for which multiple ground objects can be set and simulated by researchers themselves according to experimental demands.

Fig. 6.1 Principle comparison of simulation methods of spaceborne multispectral images and traditional multispectral images

6.1 Modeling and Simulation of Detection Images Fusion Based …

235

6.1.1 Spectral Dimensional Transformation Based on Integrated Aerospace Due to different spatial locations of various airborne and spaceborne remote-sensing platforms, electromagnetic radiation entering detectors passes through different atmospheric transmission paths, so the atmosphere exerts different influences on electromagnetic waves (EMWs) attenuation (Fig. 6.2). When transforming remotesensing images airborne to spaceborne ones, the spectral transmittance of the atmosphere above an airborne remote-sensing platform while below a spaceborne remotesensing platform directly influences spectral distribution of images acquired by various remote-sensing platforms. The basic principle of spectral transformation from airborne to spaceborne remote-sensing images is expressed as Eq. (6.1). DN_space(λ) = DN_aero(λ)T (λ)

(6.1)

where DN_space(λ) is the radiation intensity of pixels (DN value) in spaceborne remote-sensing images in the spectral band λ; DN_aero(λ) is the radiation intensity of pixels (DN value) in airborne remote-sensing images in the spectral band λ; T (λ) is the spectral transmittance of the atmosphere above airborne remote-sensing platforms while below spaceborne remote-sensing platforms. The spectral transmittance T (λ) of the atmosphere above airborne remote-sensing platforms while below spaceborne remote-sensing platforms is quantitatively calculated using the MODTRAN4 software for atmospheric absorption and scattering according to relevant parameters set. Figure 6.3 illustrates the obtained spectral curves of atmospheric spectral transmittance from an altitude of 1–100 km (the outermost atmosphere) (because atmospheric path radiation exerts slight influences on the entire atmospheric radiation transmission in short-wave band (0.45–2.5 µm) and bands in mid-infrared (IR) and far-IR atmospheric windows, influences of atmospheric path radiation on spectral transformation are ignored). Fig. 6.2 Relationship between the transmission path length of atmospheric radiation in airborne/ spaceborne remote sensing and the platform location

236

6 Detection and Processing of Synthetic Attributes of Integrated …

Fig. 6.3 Spectral curves of spectral transmittance (from 1 to 100 km, zenith angle is 20°, and atmospheric visibility is 23 km)

6.1.2 Scale-Space Transformation Based on Integrated Aerospace Imaging spectrometers on various airborne/spaceborne remote-sensing platforms differ in the field of view (FOV) and spatial resolution, and a same ground object also has different scales in different remote-sensing images. Considering this, scale-space transformation of ground objects is needed when simulating spaceborne remotesensing images using airborne ones. Here, a scale-space transformation method through bilinear interpolation based on vision bionics is proposed. Gaussian scalespace is an ideal mathematical model for simulating visual mechanism of human eyes. Previous research [1–3] has proven that under a series of reasonable assumptions proposed based on the visual mechanism of human eyes, scale transformation of Gaussian kernel functions can realize optimal simulation of the blurring degree from the near to the distant of targets. Assuming that the Gaussian scale-space of the image in a certain band of a multispectral image is defined as L (x, y, t), the scale-space can be obtained through convolution between the Gaussian kernel G (x, y, t) with a parameter t and I (x, y): { L(x, y, t) =

G(x, y, t) ∗ I (x, y) t > 0 I (x, y) t =0

(6.2)

1 −(x 2 +y 2 )/2t e 2π t

(6.3)

where G(x, y, t) =

where x, y, and t separately represent the location coordinates of pixels and scale parameter of L (x, y, t). The scale parameter t is a ratio of the spatial resolution of spaceborne remote-sensing images to that of airborne remote-sensing images. As the scale parameter t gradually increases, the blurring degree of images in different

6.1 Modeling and Simulation of Detection Images Fusion Based …

237

(A) Original image

(B) t = 9

(C) t = 36

(D) t = 182

(a) Bilinear interpolation of the original image with a factor of 0.5

(b) Bilinear interpolation of the image (t = 9) with a factor of 0.5

(c) Bilinear interpolation of the image (t = 36) with a factor of 0.5

(d) Bilinear interpolation of the image (t = 182) with a factor of 0.5

Fig. 6.4 Transformation results of the Gaussian kernel function and bilinear interpolation

scales in the scale-space rises, which enables simulation of the imaging process of targets on the retina in the case that humans have a growing distance from targets. According to the resolutions of airborne hyperspectral images and spaceborne multispectral images, bilinear interpolation is performed for corresponding targets to adjust the size accordingly. By doing so, the imaging results of the simulated targets on the low-resolution spaceborne remote-sensing platform are attained, as shown in Fig. 6.4.

6.1.3 Radiation Intensity Transformation Based on Integrated Aerospace Due to different DN values that represent radiation intensity in remote-sensing images acquired using various imaging spectrometers, the radiation intensity of specific ground objects involved in transformation needs to be transformed when simulating spaceborne multispectral images using airborne hyperspectral images. A group of target points with explicit spectral features in bands (such as three bands: red, green, and blue) in the spaceborne and airborne remote-sensing images are taken as the calibration points to calculate the proportionality factor of radiation. Then, radiation intensity of airborne remote-sensing images is transformed into that of spaceborne remote-sensing images based on the proportionality factor. White clouds in a spaceborne multispectral image and white airplanes in an airborne hyperspectral image are selected as a group of corresponding radiation calibration points. Then, the radiation intensities of white clouds and white airplanes in the visible-light band are subjected to isometric projective transformation. The green band (human eyes are most sensitive to green light) in the spaceborne multispectral image is selected as the benchmark band for radiation calibration. The ratio of this radiation intensity to the average radiation intensity (λ = 550 nm) of airplane

238

6 Detection and Processing of Synthetic Attributes of Integrated …

samples in the green band of the airborne hyperspectral image is taken as the transformation coefficient D_rate of radiation intensity. Then, the coefficient is multiplied by the spectra of scale-transformed airplane samples to transform the radiation intensity into that of the spaceborne multispectral image, thus fulfilling airborne/spaceborne transformation of radiation intensity (Figs. 6.5 and 6.6). The practical application of transformation of radiation intensity has the following limitations: ➀ transformation of radiation intensity is mainly applied to quantitative data subjected to radiation calibration; while for uncalibrated raw data, transformation of radiation intensity belongs to relative transformation and the transformed data may incur some errors to the quantitative remote-sensing analysis and application; ➁ when selecting radiation calibration points, a group of targets with similar spectral distribution in all or some bands need to be selected as calibration points, which can effectively decrease the transformation error of radiation intensity. DN_space(λ) = D_rate DN_areo(λ) Fig. 6.5 Integrated RGB color image of a spaceborne multispectral image

Fig. 6.6 Integrated RGB color image of an airborne hyperspectral image

(6.4)

6.1 Modeling and Simulation of Detection Images Fusion Based …

239

6.1.4 Mixed Pixels Transformation Based on Integrated Aerospace When simulating spaceborne remote-sensing images, the imaging area corresponding to a single pixel generally has targets and background objects at the same time, which raising the problem of blended pixels in remote-sensing images. According to spatial resolutions of spaceborne and airborne remote-sensing images, airborne remote-sensing images are segmented to separately calculate the proportionality factor of targets and background in each segmented region. It is adopted as the proportionality factor for spectral mixing of targets and background objects to achieve transformation of blended pixels in remote-sensing images. The results are illustrated in Fig. 6.7. It is assumed that the transverse and longitudinal spatial resolutions of an airborne hyperspectral image are separately row_aero_resolution and col_aero_resolution, and those of a spaceborne multispectral image are row_space_resolution and col_ space_resolution. The image segmentation force is seg_rate, which includes two parts, namely longitudinal and transverse resolutions: row_space_resolution = row_seg_rate row_aero_resolution

(6.5)

col_space_resolution = col_seg_rate col_aero_resolution

(6.6)

The image segmentation force seg_rate physically means the proportionality factor of targets and background in each row_seg_rate × col_seg_rate region when transforming row_seg_rate × col_seg_rate pixel regions in the airborne hyperspectral image into a pixel in the simulated spaceborne multispectral image. It is the proportionality factor for mixing spectra of ground targets and background in the spaceborne multispectral image. The proportionality factors for spectral mixing of results in Fig. 6.7 are listed in Table 6.1.

Original image

Image of the target area

Segmentation result A

Segmentation result B

(seg_rate=18×20)

(seg_rate=23×23)

Fig. 6.7 Original image and the segmentation results of the target area

240

6 Detection and Processing of Synthetic Attributes of Integrated …

Table 6.1 Calculation results of proportionality factors for spectral mixing (a) Proportionality factors for spectral mixing of various regions in segmentation result A 0

0.0437

0.6000

0.075

0

0.2167

0.0417

0.314

0.0333

0.469

0.0556

0.5944

0.8417

0.5889

0.61

0

0.0028

0.3528

0.0006

0

0

0

0.1571

0

0

(b) Proportionality factors for spectral mixing of various regions in segmentation result B 0.087

0.0189

0.3875

0.0246

0.1130

0.035

0.4310

0.7335

0.4423

0.0870

0

0

0.2287

0

0

6.1.5 Noise Transformation Based on Integrated Aerospace Due to different noise levels of various imaging spectrometers, to simulate remotesensing images more comprehensively, the noise in simulated images needs to be transformed according to the noise level of simulation objects. Airborne/spaceborne noise transformation mainly includes two steps: noise extraction from spaceborne multispectral images and Gaussian noise reconstruction based on characteristic parameters.

6.1.5.1

Noise Extraction from Spaceborne Multispectral Images

It is assumed that the predicted value xˆi, j,k is the DN value of a pixel at coordinates (i, j) in the kth band of a spaceborne multispectral image (here a two-dimensional (2D) array (i, j) is used to represent the pixel location in the space). To consider influences of space and spectra at the same time, the estimated DN value of pixels is

xˆi, j,k

⎧ k=1 ⎨ axi, j,k+1 + cx p,k + d = axi, j,k−1 + bx p,k+1 + cx p,k + d 1 < k < N ⎩ k=N axi, j,k−1 + cx p,k + d

(6.7)

where { x p,k =

xi−1, j,k i > 1 xi+1, j,k i = 1

1 ≤ i ≤ W, 1 ≤ j ≤ H

(6.8)

where W and H denote the width and height of images; N is the total number of bands. Then the residue is r = x − xˆ

(6.9)

6.1 Modeling and Simulation of Detection Images Fusion Based …

241

above is the estimate of noises extracted from each pixel. For the expectation Σ The r 2 = 0, the optimal unbiased estimator xˆi, j,k of xi, j,k can be attained through linear regression using Eq. (6.7). Meanwhile, coefficients [a, b, c, d] can alsoΣbe obtained by minimizing noise variances in each band, that is, minimizing S 2 = r 2 . A total of M = W × H − 1 equations in the kth band will participate in the linear regression (the first pixel does not take part in regression), and the calculation formula is W [a, b, c, d]T = x ⇒ [a, b, c, d]T = W T (W W T )−1 x

(6.10)

where x [is a vector composed of all ] pixels xi, j,k in the band; W is a matrix composed of vectors xi, j,k−1 , xi, j,k+1 , x p,k , 1 ; k is a constant; i and j are variables. After solving the coefficient of each band, the covariance matrix of noises in the hyperspectral image can be calculated on this basis. Then, the variance in the kth band and the covariance between the kth and lth bands can separately be calculated using the following equations [4] W Σ H Σ

σk2

=

6.1.5.2

i=1 j=1

W Σ H Σ

ri,2 j,k

M −4

, Ckl =

ri, j,k ri, j,l

i=1 j=1

M −4

1 ≤ k, l ≤ N , (i, j ) /= (1, 1) (6.11)

Gaussian Noise Reconstruction Based on Characteristic Parameters

Analysis of various imaging spectrometers reveals that the noises in remote-sensing images are commonly Gaussian noises. Combining with noise parameters σk2 and Ckl of the spaceborne multispectral image extracted above, the 3D noise matrix based on Gaussian distribution is constructed. By adding the hyperspectral noise into the airborne hyperspectral image, the airborne/spaceborne noise transformation can be fulfilled.

6.1.6 Simulation Analysis In the simulation experiments in the section, the airborne hyperspectral images were images of an airport in San Diego (USA) that cover a band range of 0.4–2.5 µm and have spatial resolution of 3.5 m collected using AVIRIS [5]. After eliminating bands with water-vapor absorption and severe noises, 189 bands were selected as the research objects. For spaceborne multispectral images, the first six bands of images of Kilauea Volcano in Hawaii taken using Landsat 7ETM+ were used as the baseline simulation data (the seventh band was not simulated because it was a far-IR band

242

6 Detection and Processing of Synthetic Attributes of Integrated …

(10.4–12.5 µm) not covered by the hyperspectral images), the spatial resolution of which was 30 m. Figure 6.8 illustrates spectral curves of airplane samples in the airborne hyperspectral remote-sensing images. All samples in the simulation experiments were subjected to spectral transformation to attain spectral distribution of airplanes observed from a spaceborne remote-sensing platform. Figure 6.9 shows comparison of spectral distribution of airplane sample points (referring to [32, 64] for the specific coordinates) before and after atmospheric attenuation. Airplane sample points were subjected to airborne/spaceborne scale-space transformation. Then, average spectra of transformed airplane samples in corresponding bands were calculated according to band distribution of spaceborne multispectral images, thus attaining spectral curves of airplanes experiencing scale-space transformation in Fig. 6.10. Figure 6.11 illustrates the stereogram for Gaussian noises reconstructed using relevant parameters extracted according to the spaceborne multispectral images in airborne/spaceborne noise transformation. Figure 6.12 shows distribution of spectral curves of airplane samples after airborne/spaceborne transformations of radiation intensity, blended pixels, and noises. After radiation calibration of white clouds in the second band, the DN value of airplane samples in the second band changes from 4745 in the hyperspectral image to 204, which is same as that of white clouds in the second band of the real spaceborne multispectral images. Then, transformations of blended pixels and noises were performed to calibrated spectra of airplanes, thus attaining the finally simulated spectral curves of airplanes. Figure 6.13 shows the image in the second band (λ = 0.52−0.60 µm) of the simulated spaceborne multispectral images. The figure includes white calibration points, directly projected airplane samples, and finally obtained target pixels after airborne/spaceborne transformations using the methods proposed in the section. Fig. 6.8 Spectral curves of airplane samples in airborne hyperspectral remote-sensing images

6.2 Combined Ant Colony Optimization of Spatial-Spectral 2D Features …

243

Fig. 6.9 Comparison of spectral distribution of airplane sample points before and after atmospheric attenuation

Fig. 6.10 Spectral curve of airplanes obtained after airborne/spaceborne scale-space transformation (scale parameter 30 = 8.57) t = 3.5

Fig. 6.11 Stereogram for the Gaussian noise matrix (the second band, σ = 3.7)

6.2 Combined Ant Colony Optimization of Spatial-Spectral 2D Features in Detection Images Airborne/spaceborne integrated detection images of targets show large data redundancy. Existing optimization and processing methods fail to accurately extract characteristic bands containing much target information from such images. Training

244

6 Detection and Processing of Synthetic Attributes of Integrated …

Fig. 6.12 Spectral curves of airplanes after each transformation in experiments in the section (D_rate = 0.043)

Fig. 6.13 Simulated spaceborne multispectral image (the second band: 0.52–0.60 µm)

samples are generally used in target classification and identification. If there are heterogeneous samples in training samples, the spectral features of targets cannot be accurately reflected, which directly impairs the classification and identification precision. The section explains a data optimization method based on combined ant colony optimization (ACO) of spatial-spectral 2D features and verifies effectiveness of the proposed method by carrying out two groups of simulation experiments.

6.2.1 Combined ACO of Spatial-Spectral 2D Features Training samples are frequently used in supervised classification. When selecting training samples, some selected training samples cannot accurately characterize

6.2 Combined Ant Colony Optimization of Spatial-Spectral 2D Features …

245

spectral features of a class of samples due to presence of imaging noises and influences of blended pixels. These samples are called heterogeneous samples [6]. In a high-dimensional feature space, heterogeneous samples are commonly distributed on the margins of accumulation space of training samples and they intensively show the characteristic of “a same object has different spectra” in hyperspectral images. If training various target detectors or classifiers using heterogeneous samples, the sorting error incurred by imaging noises and blended pixels is brought into detectors or classifiers [7], which affects the target detection and identification precision. Optimization of distribution features in a sample space is a process to remove heterogeneous samples from the training sample set and optimize feature space distribution of samples. Optimizing spatial distribution of training samples can eliminate influences of heterogeneous samples distributed on the margins of accumulation space of training samples in the feature space on subsequent processing of detection data and improve target detection and identification precision of classification and identification algorithms.

6.2.1.1

Basic Principle of ACO

ACO is a bionic swarm intelligent optimization algorithm that imitates the foraging behaviors of ants [8], and it is characterized by self-organization, robustness, positive feedback, and parallelism. To further explain the basic principle and mathematical model of ACO, the basic principle of the entire algorithm is analyzed combining with the schematic diagrams for the motion of ants between a food source and a nest (Fig. 6.14). In Fig. 6.14, Vd and Vs separately represent the food source and the ant nest, between which there are two paths E 1 and E 2 with the lengths separately of L 1 and L 2 (L 1 < L 2 ). A virtual ant is represented as m a . In the initial state, the probabilities of ants for selecting the two paths are same, that is, p1 = p2 = 1/2. After multiple round trips of ants between the food source and the nest, the pheromones on each path begin to change. Assuming that pheromones on paths E 1 and E 2 change into τ1 and τ2 , then any virtual ant departing from the nest 1 2 to select path E 1 and a probability of p2 = τ1τ+τ Vs has a probability of p1 = τ1τ+τ 2 2 to select path E 2 . The more the pheromones on the path are, the larger the probability that the path is selected by the virtual ant. When the ant gets food and returns from the food source, pheromones on the paths separately vary into τi = (1 − ρi )τi , i = 1, 2 due to pheromone volatilization, in which ρi ∈ (0, 1] is the pheromone volatilization coefficient. The value of the coefficient is inversely proportional to the time taken by the ant to pass through the path while directly proportional to the path length (assuming that the ant velocity is fixed). Therefore, the ant also tends to select the path of a larger probability when returning to the nest. After one round trip of the virtual ant, the pheromone on the paths changes into τi ← τi + Qli , in which i = 1, 2 and Q is a constant greater than 0. After multiple round trips of lots of virtual ants between the food source and the nest, the shorter path contains increasingly more pheromones and more ants select the path. It can be seen from Fig. 6.14d that after multiple round trips, there are few ants on the path E 2 with a longer distance and

246

6 Detection and Processing of Synthetic Attributes of Integrated …

Fig. 6.14 Schematic diagrams for the motion of ants between the food source and the nest

the pheromone concentration thereon is also very low. Most virtual ants select the optimal path E 1 with a shorter distance. In the practical application of ACO, various feature combination sets are equivalent to the paths selectable in the feature space and each path is equivalent to a solution in the feature space. The algorithm finally forms the optimal path and obtains the optimal feature combination, namely the optimal solution by constructing a virtual ant colony to search and transfer in the feature space. Individual ants in the ant colony select their own transfer paths according to the expected value of feature transfer and pheromones left by other ants in the feature space [9].

6.2.1.2

ACO Modeling of the Feature Space

The ACO model of spatial-spectral 2D features was established according to the basic principle of ACO introduced above. Two ant colonies were designed to alternately search in their individual feature spaces to find the optimal feature combinations. To unify the entire ACO, various spectral combinations and sample combinations were uniformly abstracted as feature combinations or feature sets during modeling. In addition, the ACO model was built in the feature space, thus attaining the general formula for transfer of the two ant colonies in the feature space. In the ACO modeling, the virtual feature space for ants to search and transfer was constructed at first. Assuming that Z is the original data set containing W features, that is, Z = {z 1 , z 2 , . . . , z W }, ACO mainly aims to search for the data set S consisting of d features so that the discrimination function J reaches the extreme point.

6.2 Combined Ant Colony Optimization of Spatial-Spectral 2D Features …

247

(6.12) where (z i1 , z i2 , . . . , z id ) ⊆ (z 1 , z 2 , . . . , z W ), and the feature data set can be bands or training samples. According to the basic principle of ACO, pheromones on each path vary constantly with the motion of ants in the transfer process of ants in the feature space. Assuming that the kth ant Antk (k = 1, 2, . . . , m) is in the feature space z i , then ant Antk decides the probability to transfer to the next state z j according to pheromones on each selectable path and the expected value. pikj (t) =

⎧ ⎨Σ ⎩0

β

τiαj (t)ηi j (t)

s∈allowedk

β

τisα (t)ηis (t)

j ∈ allowedk

(6.13)

otherwise

where allowedk is the candidate feature set allowed to be selected in the next transfer of ant Antk and allowedk = {Z − tabuk }, in which Z is the universal set and tabuk is the tabu list that represents feature states experienced by ant Antk ; α and β separately denote the heuristic coefficients of information and expectation; τi j (t) is the pheromone concentration; ηi j (t) is the expectation function of transferring from feature i to j and can be expressed by ηi j (t) = Ji j ; Ji j is the discrimination function between features i and j. For any ant Antk , the larger the discrimination function Ji j is, the greater the expectation function ηi j and the higher the probability pikj (t) for selecting the path. Correspondingly, the pheromone concentration τi j (t) on each path also varies when ants migrate in the feature space. After each ant transfers for one time in the whole ant colony, the pheromone concentrations on each path are adjusted using the following equation: τi j (t + 1) = (1 − ρ)τi j (t) + Δτi j (t) Δτi j (t) =

m Σ

Δτikj (t)

(6.14)

k=1

where ρ is the pheromone volatilization coefficient; τi j (t) is the pheromone increment on path (i, j ) in the tth cycle; τikj (t) is the pheromone left by the kth ant on the path (i, j) after passing through the path in the tth cycle. { τikj (t) =

Q , Lk

the kth ant in this cycle passes through the path(i, j) 0, else

(6.15)

where Q is a positive constant to indicate the overall pheromone intensity; L k is the length of path that ant Antk passes through in the state transition.

248

6 Detection and Processing of Synthetic Attributes of Integrated …

After multiple iterative searches of the ant colonies, the feature subset corresponding to the optimal discrimination function J is the optimal processing results of the algorithm.

6.2.1.3

Discrimination Functions for Optimization of Spatial-Spectral 2D Features

The traditional ACO generally can only optimize a single feature. However, probably more than one feature needs to be optimized during processing of many hyperspectral images; or the processing does not aim to optimize a certain feature, but expects to optimize comprehensive processing results of a system while multiple features are suboptimal. Considering this, training samples and spectral bands are separately taken as features on the basis of ACO to perform combined ACO, eliminate heterogeneous samples and bands with redundant data, and achieve comprehensive optimization of hyperspectral images. Here, two ant colonies are constructed at first to alternately search the band combinations with the maximum inter-class distance and heterogeneous samples separately in the spectral space and sample distribution space of hyperspectral data. Relevant parameters and discrimination functions are defined as follows: For spectral optimization, letting x i and x j separately be image vectors in the bands i and j: x i = [xi1 , xi2 , . . . , xi L ]T and x j = [x j1 , x j2 , . . . , x j L ]T , in which L is the total number of pixels in single-band images (L = image width × image height). The corresponding subscript in the subsequent symbols is spectral. In terms of spatial distribution of training samples, the training sample points in the hyperspectral images are numbered as 1 . . . Mspatial , and the total number of samples is Mspatial . Letting yi and y j separately be spectral vectors of samples i and j, i, j ∈ {1 . . . Mspatial }, yi = [yi1 , yi2 , . . . , yi N ]T , and y j = [y j1 , y j2 , . . . , y j N ]T ; N is the number of bands in hyperspectral images. The corresponding subscript in the subsequent symbols is spatial. The Bhattacharya distance is used as the spectral discrimination function. JBH-spectral

( )−1 1 1 |(Σ1 + Σ2 )/2| T Σ1 + Σ2 = (μ2 − μ1 ) (μ2 − μ1 ) + In 8 2 2 |Σ1 |1/2 · |Σ2 |1/2 (6.16)

For the optimization of samples in the space, the spectral angle mapping (SAM) is adopted as the discrimination function. ( JSAM-spatial = arccos

(y1 , y2 ) ||y1 ||||y2 ||

)

⎛ = arccos⎝ /Σ

ΣN n=1 N n=1

y1n

⎞ y1n y2n /Σ N

n=1 y2n

⎠

(6.17)

6.2 Combined Ant Colony Optimization of Spatial-Spectral 2D Features …

6.2.1.4

249

Design and Implementation of Combined ACO

The basic procedures of combined ACO are shown in Fig. 6.15, the left part of which is the spectral optimization procedure, and the right part is the optimization procedure of sample spaces. In the optimization procedures of spectra and sample spaces, the spectral feature set (X − S max-spectral ) and the training sample set (Y − S max-spatial ) change alternately. When a certain ant colony optimizes a class of features, the other class (spectral dimension or training sample set) remains fixed. On this basis, the discrimination function for this class of features is solved.

Fig. 6.15 Procedures of combined ACO

250

6 Detection and Processing of Synthetic Attributes of Integrated …

The specific steps of combined ACO include: Step 1: Parameters αspectral , αspatial , βspectral , βspatial , τspectral , τspatial , rspectral , and rspatial are normalized, and the cycle number is set to be Nc = 0. Ncmax is the maximum total cycle number of the system. The initialized information quantities are τi j-spectral (0) = const and τi j-spatial (0) = const; τi j-spectral (0) = τi j-spatial (0) = 0 at the initial moment; the subspace size of characteristic bands is Mspectral ; the total number of training sample is Mspatical ; Jmax-spectral = 0; and Jmax -spatial = 0. Step 2: Correlation coefficients ri j of various bands are calculated, and bands of low correlation coefficients are selected to construct a candidate band set (generally, bands of large correlation are likely to trap the algorithm into local optimum, so bands of low correlation coefficients are selected in priority). The proportionality factor γ of heterogeneous samples is normalized, and the number of heterogeneous samples in the initial state is 0. Step 3: Cycle number is Nc = 1. Step 4: Cyclic ACO of spectra is carried out. Step 4.1: The serial number of an ant in the spectral dimension is set to be kspectral = 1. Step 4.2: Individual ants depart randomly from a characteristic band xi . Step 4.3: The kspectral th ant in the spectral dimension( calculates the state}(transi{ tion probability pikj (t) from characteristic band x i to x j x j ∈ X − Sspectral using Eq. (6.15). Step 4.4: Candidate bands are selected according to the state transition probability (bands with a larger state transition probability have a greater probability to be selected). If a candidate band searched has a correlation coefficient lower than the correlation threshold rspectral with characteristic band x i at the moment that the ant departs, the candidate band is added in a characteristic band subset Sk-spectral ; otherwise, the next characteristic band is searched. Step 4.5: If the number of characteristic bands in the characteristic band subset Sk-spectral is smaller than the pre-set number K spectral of extracted bands (K spectral = Mspectral = in the entire ACO), the ant continues to search; otherwise, the next step is implemented. Step 4.6: Jk-spectral = max(JBH-spectral ) is calculated using various training samples (Y − Smax-spatial ) after eliminating heterogeneous samples. If all ants in the spectral dimension have finished search, the next step is implemented; otherwise, kspectral = kspectral + 1 and it turns to Step 4.3. Step 4.7: All ants in the spectral dimension move to their individual next state x j , and it is changed that i = j. Then, the information quantity on each path is updated using Eqs. (6.16) and (6.18), in which L k-spectral = Jk-spectral . Step 4.8: Letting the maximum discrimination function of the ant colonies be JNc -spectral = max(Jk-spectral ), if JNc -spectral > Jmax-spectral , then Jmax-spectral = JNc -spectral and characteristic band subset Smax-spectral = Sk-spectral of corresponding ant in the spectral dimension is retained. Step 5: ACO cycles are performed for spatial distribution of training samples. Step 5.1: The serial number of an ant in the sample space is set to be kspatial = 1.

6.2 Combined Ant Colony Optimization of Spatial-Spectral 2D Features …

251

Step 5.2: Individual ants in the sample space depart randomly from a training sample yi . Step 5.3: The state transition probability pikj (t) from sample yi to y j of the kspatial th ( }( { ant in the sample space is calculated using Eq. (6.15) y j ∈ Y − Sspatial . Step 5.4: A candidate sample is selected according to the state transition probability. If JSAM-spatial between the candidate sample and the training sample is larger than the threshold rspatial , the searched candidate sample is added in the sample subset Sk-spatial ; otherwise, the next sample is selected. Step 5.5: If the number of samples in the sample subset Sk-spatial is smaller than the pre-set number K spatial of heterogeneous samples, ants in the sample space continue to search; otherwise, the next step is implemented. Step 5.6: The maximum discrimination function Jk-spatial = max(JSAM-spatial ) of the sample subset is solved in the current spectral space (X − Smax-spectral ). If all ants in the sample space have finished search, the next step is implemented; otherwise, it turns to Step 5.3. Step 5.7: All ants in the sample space move to their next state y j , and it is changed that i = j. In addition, the information quantity on each path is updated, in which L k-spatial = Jk-spatial . Step 5.8: Letting the maximum discrimination function of the ant colonies be JNc -spatial = max(Jk-spatial ), if JNc -spatial > Jmax-spatial , then Jmax-spatial = JNc -spatial and the heterogeneous sample combination Smax-spatial = Sk-spatial of corresponding ant is retained. Step 6: Nc = Nc + 1. Step 7: If the cycle number is Nc > Ncmax , the cycles are ended, and the calculation result Jmax-spectral , the corresponding characteristic band subspace, and corresponding heterogeneous samples are output; otherwise, characteristic band subspaces and heterogeneous samples are emptied and it turns to Step 4.

6.2.2 Simulation Experiments and Analysis To analyze the optimization effect of combined ACO, simulation experiments on single-colony ACO and combined ACO were conducted. In the simulation experiment on single-colony ACO, features in one dimension were fixed at first to simulate single-colony ACO on features in another dimension; in simulation experiments on combined ACO, combined ACO was conducted on spectra and training samples at the same time.

6.2.2.1

Simulation Experiments on Single-Colony ACO

At first, single-colony cycles were used to perform optimization experiments on hyperspectral data. During spatial optimization of samples, the spectral dimension

252

6 Detection and Processing of Synthetic Attributes of Integrated …

is not changed. During spectral optimization, the number and location of samples are not changed. The hyperspectral data were scanning images of Sandi ego naval experimental base using AVIRIS. After eliminating bands with a low signal-to-noise ratio (SNR) and severe water-vapor absorption, 189 bands were left. Figure 6.16 shows the original image in the second band of the hyperspectral images. Figure 6.17 displays the spatial distribution of the selected training samples (white areas indicate airplanes, gray areas enclosed with white boxes are runways, and white areas with oblique lines are wastelands), which contain 71 airplane samples, 56 runway samples, and 56 wasteland samples. Figure 6.18 illustrates distribution of spectral curves of three classes of training samples, as well as spatial distribution and spectral distribution of heterogeneous airplane samples searched using ACO. Coordinates of heterogeneous airplane samples are (21, 48), (21, 56), and (26, 67). Figure 6.18 shows that heterogeneous sample points are mainly blended pixels at the margins of airplane targets in the samples. Such blended pixels, as training samples, enlarge the feature space of Fig. 6.16 Original image in the second band of hyperspectral images

Fig. 6.17 Spatial distribution of the selected training samples

6.2 Combined Ant Colony Optimization of Spatial-Spectral 2D Features …

253

Fig. 6.18 Distribution of spectral curves of the three classes of training samples as well as spatial distribution and spectral distribution of heterogeneous airplane samples

training samples of target classes and reduce the target classification and identification precision. In spectral optimization experiments, the spatial distribution of training samples is fixed at first, then the band combination with the maximum inter-class distance is searched as the optimal characteristic band using ACO of spectra. Table 6.2 lists the distribution of optimal characteristic bands extracted using ACO. It can be seen from the table that with the different numbers of bands extracted using ACO, the band combination optimized by the algorithm also differs. When increasing the number of extracted bands, the maximum Bhattacharya distance between various classes of samples also enlarges. To further expound advantages of ACO, ACO is compared with other four dimensionality-reduction or band-extraction algorithms, including principal component analysis (PCA), minimum noise fraction (MNF), adaptive subspace decomposition (ASD), and sequential floating forward search (SFFS). In the simulation experiments, the number of extracted bands was uniformly set to be K spectral = 10. PCA and MNF, proposed for dimensionality reduction based on spectral transformation, mainly select first ten bands with high information contents after transformation as extracted bands. ASD, SFFS, and ACO are search-type band-extraction algorithms. The information entropy [10] and optimum index factor (OIF) [11] were mainly used as criteria for evaluating the five optimization algorithms for dimensionality reduction. The larger the value of information entropy is, the more abundant the information quantity in images, which is more conducive to the subsequent classification and identification of hyperspectral images. OIF is a measure for the comprehensive

254

6 Detection and Processing of Synthetic Attributes of Integrated …

Table 6.2 Distribution of optimal characteristic bands extracted using ACO Numbers of extracted bands K spectral

Serial numbers of bands extracted using ACO (under fixed spatial Maximum distribution of training samples) JBH-spectral

10

2, 12, 19, 56, 75, 89, 120, 145, 154, 169

15

1, 2, 5, 47, 60, 62, 63, 93, 98, 115, 122, 124, 132, 133, 135

20

1, 2, 6, 29, 39, 64, 78, 80, 85, 88, 109, 114, 121, 148, 150, 154, 162, 166, 172, 183

25

2, 9, 18, 24, 36, 48, 54, 58, 63, 70, 81, 94, 102, 105, 120, 123, 136, 11.6833 137, 141, 150, 164, 167, 171, 179, 183

30

4, 7, 14, 17, 30, 31, 40, 48, 54, 60, 62, 63, 76, 83, 86, 91, 96, 98, 108, 114, 121, 124, 132, 139, 149, 153, 165, 170, 175, 183

6.6650 8.3641 10.2498

13.3297

information quantity in multiband combined images. The greater the OIF is, the more the information contained in image combinations. OIF is expressed as follows: Σp σi | | OIF = Σ p Σi=1 p | | i=1 j=i+1 ri j

(6.18)

where σi is the standard deviation of the ith band; ri j is the correlation coefficient between any two bands i and j. Distribution of information entropies of extracted bands after optimization using the five algorithms is shown in Fig. 6.19. Hyperspectral images after dimensionality reduction using PCA generally contain less information, which also fluctuates greatly. Information entropies in some bands are low, with a serious information loss, and the information quantity in extracted bands is not stable on the whole. The first three bands extracted using MNF exhibit favorable information entropies while the information quantity in extracted bands also changes greatly, particularly the ninth band extracted, the information entropy of which approximates 5.0, indicating an unsatisfactory information extraction effect. Information entropies of bands extracted using ASD, SFFS, and ACO are stable. In comparison, the information entropy of bands extracted using ACO (the line with rhombic points) is distributed above the other two curves, which suggests that bands extracted using ACO show stable information entropies and contain ample information, so ACO is a favorable optimization algorithm for dimensionality reduction. OIFs for band combinations extracted using the five algorithms are listed in Table 6.3. Correlated multiband images in hyperspectral images become uncorrelated after being transformed using PCA and MNF, so the denominator in the formula of OIF tends to be zero and therefore the entire OIF tends to be infinite without reference significance. Considering this, comparison of the OIF of ACO with those of ASD and SFFS is highlighted. Table 6.3 shows that OIF for band combinations extracted using ACO is larger than those of ASD and SFFS, so band combinations extracted using ACO contain

6.2 Combined Ant Colony Optimization of Spatial-Spectral 2D Features …

255

Fig. 6.19 Distribution of information entropies of extracted bands after optimization using the five algorithms

Table 6.3 OIFs for band combinations extracted using the three algorithms

Optimization algorithms

ASD

SFFS

ACO

OIF

0.2503

0.2477

0.2611

more information in the low-dimensional space and ACO is a favorable optimization algorithm for dimensionality reduction.

6.2.2.2

Simulation Experiments of Combined ACO

Simulation experiments of single-colony ACO reveal that ACO performs well in optimization for dimensionality reduction. Here, combined ACO was used to carry out combined optimization on spectra and training samples simultaneously in hyperspectral images. In simulation experiments, due to absence of a unified standard for evaluating the comprehensive optimization effect of the two groups of features, classification results of a classical classification algorithm were used to indirectly measure the comprehensive optimization effect of combined ACO. The same classification algorithm was used to conduct classification experiments on optimization results of combined ACO, optimization results of other algorithms, and unoptimized hyperspectral data samples. The classification precisions were compared. The higher the classification precision is, the better the optimization effect of the algorithm used in the early stage; otherwise, the poorer the effect of the optimization algorithm [12]. Moreover, for detection of synthetic attributes in images, optimization of detection images also aims to provide service for subsequent target classification and identification algorithms, so as to achieve the goal of improving the target classification and identification precision. Therefore, it is reasonable and feasible to indirectly compare and measure optimization effects of various algorithms using the classification and detection precision.

256

6 Detection and Processing of Synthetic Attributes of Integrated …

The five algorithms selected in the experiments included classification algorithms of hyperspectral images separately based on support vector machine (SVM) (unoptimized raw data were directly classified using SVM), dimensionality reduction by PCA and SVM (PCA-SVM), Markov sample optimization and SVM (MK-SVM), isometric mapping (Isomap) for manifold learning algorithm [13] and SVM (IsomapSVM), and combined ACO for spatial-spectral 2D features and SVM (ACO-SVM). Using the classical SVM-based classification algorithm of hyperspectral images as the basic algorithm, the five algorithms were compared. Distribution of training samples is displayed in Fig. 6.17 and that of test samples is shown in Fig. 6.20 (in which, white areas, gray areas, and white areas with oblique lines separately represent airplane, runway, and wasteland areas). The figure contains 125 airplane samples, 334 runway samples, and 244 wasteland samples. Combined ACO was used for combined optimization of spectral features and spatial distribution of samples. Relevant parameters were set as follows: ρ = 0.1, αspectral = 1, αspatial = 1, βspectral = 2, βspatial = 2, τi j-spectral (0) = 1, τi j-spatial (0) = 1, γ = 2%, γspectral = 0.97, and γspatial = 0.8; the numbers of ants in the two colonies were m spectral = m spatial = 20, the maximum cycle number of ants was Ncmax = 40, and the number of target bands expected to be extracted was K spectral = 10. A polynomial kernel function was applied as the kernel function of SVM. Table 6.4 shows band distribution obtained after combined optimization of spatialspectral features using combined ACO. Ten representative bands were extracted from the original spectral images through optimization and dimensionality reduction, which retained optimal feature combinations and reduced subsequent computation burden for classification of hyperspectral images. Figure 6.21 shows the distribution of heterogeneous airplane sample points obtained after optimizing spatial-spectral features using combined ACO. In the figure, the white pixels with * are heterogeneous sample points calculated, the coordinates of which are (21, 48), (21, 56), and (30, 64), respectively. By screening heterogeneous samples in the training samples, the influence of noise sample points on the Fig. 6.20 Distribution of test samples

6.2 Combined Ant Colony Optimization of Spatial-Spectral 2D Features …

257

Table 6.4 Distribution of bands attained through combined optimization of spatial-spectral features using combined ACO New serial numbers of bands after combined 1 2 3 optimization Serial numbers of bands in original images

4

5

6

7

8

9

10

2 7 23 63 78 87 98 118 131 157

Fig. 6.21 Distribution of heterogeneous airplane sample points after combined optimization

classifier is decreased and training samples can more accurately reflect target features, which helps improve the classification precision of algorithms used subsequently. Figure 6.22 illustrates 2D scatter distribution of sample points and support vectors when classifying airplane targets using unoptimized SVM and ACO-SVM. It can be seen from the figure that optimization of spectra and training samples using ACO actually extends the distance of various classes of training samples in the feature space, which provides assistance for SVM to find various support vectors that can accurately classify targets and background. Figures 6.23, 6.24, 6.25, 6.26 and 6.27 separately show classification results of airplanes, runways, and wastelands using the five algorithms. Table 6.5 compares the classification effects of the five algorithms. It can be seen from Figs. 6.23, 6.24, 6.25, 6.26, 6.27 and Table 6.5 that three algorithms, namely SVM, MK-SVM, and ACO-SVM accurately classify most pixels while with different classification precisions. PCA-SVM is found to have a poor classification effect because information of images transformed by PCA mainly reflects the value of image variance, so that the classification algorithm fails to distinguish pixels of airplanes, runways, and wastelands. There are lots of noise points in the classification results of unoptimized SVM because heterogeneous samples in the training samples have brought noises to the classification algorithm. At the same time, the large redundancy of spectral data also affects the classification accuracy. Isomap-SVM first uses Isomap for manifold learning to reduce dimensions of hyperspectral images and then adopts SVM for

258

6 Detection and Processing of Synthetic Attributes of Integrated …

Fig. 6.22 2D scatter distribution of sample points and support vectors during classification of airplane targets

classification. The figures and table show that Isomap-SVM performs well in classifying airplane targets while poorly in classifying runways and wastelands. This is mainly because hyperspectral features of runways and wastelands are weakened after dimensionality reduction using Isomap. Training samples of MK-SVM are obtained via transfer transformation of pure target samples using the Markov model, so the samples can well reflect basic features of various classes of targets, while the spectra are not optimized and show some misclassification points. The classification results of the ACO-SVM proposed in the section are displayed in Fig. 6.27, which shows that ACO-SVM can accurately classify pixels of various classes of targets. It can be seen from Table 6.5 that the overall classification precision of ACO-SVM reaches 95.45% and the Kappa coefficient of ACO-SVM is 0.9252, which are higher than those of the other four classification algorithms. The results suggest that combined ACO of spatial-spectral 2D features indeed substantially improves the classification precision of classification algorithm for targets in hyperspectral images. In addition, it is a combined optimization method that performs well in both extraction of characteristic bands and optimization of training samples of hyperspectral images.

(a) Airplanes

(b) Runways

(c) Wastelands

Fig. 6.23 Classification and identification results for three classes of targets using SVM

6.2 Combined Ant Colony Optimization of Spatial-Spectral 2D Features …

(a) Airplanes

(b) Runways

259

(c) Wasteland

Fig. 6.24 Classification and identification results for three classes of targets using PCA-SVM

(a) Airplanes

(b) Runways

(c) Wastelands

Fig. 6.25 Classification and identification results for three classes of targets using Isomap-SVM

(a) Airplanes

(b) Runways

(c) Wastelands

Fig. 6.26 Classification and identification results for three classes of targets using MK-SVM

(a) Airplane

(b) Runway

(c) Wasteland

Fig. 6.27 Classification and identification results for three classes of targets using ACO-SVM

260

6 Detection and Processing of Synthetic Attributes of Integrated …

Table 6.5 Comparison of classification results of the five algorithms Classification precisions

False alarm rates

Classes

SVM

PCA-SVM

Isomap-SVM

MK-SVM

ACO-SVM

Airplanes

99.20%

72.00%

92.00%

99.20%

96.80%

Runways

87.43%

21.26%

25.15%

89.22%

95.81%

Wastelands

97.54%

0.41%

85.25%

96.72%

94.26%

Mean

94.72%

31.22%

67.47%

95.05%

95.62%

Overall precision

93.03%

23.04%

57.89%

93.60%

95.45%

Airplane s

36.00%

38.40%

44.80%

24.00%

8.00%

Runways

1.20%

4.19%

4.19%

0.90%

9.28%

Wastelands

0%

34.84%

77.05%

1.64%

3.28%

Mean

12.40%

25.81%

42.01%

8.85%

6.85%

Total false alarm rate

6.97%

20.91%

36.70%

5.26%

6.97%

0.8910

0.1773

0.4129

0.9005

0.9252

Kappa coefficients

6.3 Classification of Detection Images Based on Artificial Immune Network Supervised classification of detection images can precisely find and recognize a same class of target points or pixels with high similarity to training samples or prior feature templates, so the classification of detection images plays an important role in processing of target detection data. The section introduces the classification principle of detection images using artificial immune network (AIN) and projects hyperspectral training samples into a high-dimensional space using the nonlinear kernel function on the basis of the intelligent AIN model. The method improves the similarity-based sorting method in the kernel space of target samples of AIN, reduces the number of antibodies needed by AIN to identify samples, and increases classification and identification precision of AIN for detection targets as well as the operation speed.

6.3.1 Modeling and Kernel Space Mapping of AIN 6.3.1.1

Principle of Biological Immune Network

The natural immune system is generally composed of immune cells, proteins, and protective tissues. When foreign bacteria and viruses (commonly called antigens ag) invade tissues, the immune system firstly prevents these bacteria and viruses physically from entering tissues through the outer protective tissues. For antigens that have invaded in bodies, B cells or B lymphocytes in tissues begin to secrete antibodies corresponding to invasive antigens to prime cell-mediated immunity. The process is described as follows according to the clonal selection principle [14]: when antigens appear, B cells recognize the antigen showing the highest affinity with themselves

6.3 Classification of Detection Images Based on Artificial Immune Network

261

at first, followed by clonal propagation of B cells that recognize the antigen. In the clonal propagation process, neoplastic B cells begin to mutate under stimulation of various antigens. Finally, B cells are mature after multigenerational reproduction and mutation and eventually transformed into antibodies, that is, plasma cells. Some active B cells showing high antigen affinity are gradually transformed into memory cells, which have a longer life cycle and respond rapidly to produce corresponding antibodies at next invasion of similar antigens. To further explain the memory and learning ability of the immune system, Jerne proposed the principle of immune network [15]. Different from the above clonal selection, the immune network assumes that the immune system not only has the affinity recognition effect between antibodies and antigens but also mutual recognition between antibodies. If an antigen ag is recognized by an antibody ab1 , another antibody ab2 can recognize the antibody ab1 , and an antibody ab3 can recognize the antibody ab2 , then the antibody ab3 can recognize the antigen ag via antibodies ab2 and ab1 through forward excited propagation among the above antibodies. Interactions of an antigen with antibodies are illustrated in Fig. 6.28. The number of similar antibodies can be effectively reduced through reversed inhibitory propagation among antibodies, so as to remain the number of antibodies in an approximate range. Finally, the antibody cluster tends to stabilize and those stabilized antibodies are transformed into memory antibodies, which can effectively cover the entire antigen space and realize recognition of all antigens.

Fig. 6.28 Interaction principle between an antigen and antibodies

262

6.3.1.2

6 Detection and Processing of Synthetic Attributes of Integrated …

Modeling of Basic Elements of AIN

AIN is mainly composed of antigens, antibodies, and mutated antigens. When initializing AIN, antigens input in AIN mainly are spectral vectors of various classes of known target samples. After training the antibodies, the input antigens mainly are spectral vectors of various pixels or sample points in unknown classes in hyperspectral images when classifying and identifying targets using AIN. The output is the classes of various antigens. Mutated antigens are immature antibodies produced in the AIN training process. The antigens, antibodies, and mutated antigens are modeled according to Tables 6.6, 6.7 and 6.8, and their specific structures are described as follows: The following statements are made for relevant parameters and concepts in the model structures: the class ag.c of antigens and the serial number ab.c of main classes of antibodies represent the classes of antigens and antibodies; the serial number ab.cc of subclasses of antibodies is the serial number of } different subclasses in a main class { ag.V = ag.v , ag.v , . . . , ag.v is the spectral vector of antigens; of spectra; 1 2 N b } { ag.W = ag.w1 , ag.w2 , . . . , ag.w Nb is the central spectral vector of antibodies; Nb is the number of bands in hyperspectral images. In target classification, W determines the spatial locations of spectra of an antibody ab; σ decides the recognition range of an antibody ab. Each antibody ab in an antibody Table 6.6 Model structure of antigens

ag.num: serial number 1 . . . n of antigens r: antigen recognition tags c: class of antigens V: spectral vector of antigens

Table 6.7 Model structure of antibodies

ab.num: serial number 1 . . . n of antibodies c: serial number of main classes of antibodies cc: serial number of subclasses of antibodies W: central spectral vector of antibodies σ: antibody recognition radius

Table 6.8 Model of mutated antigens

mu.num: serial number 1 . . . n of mutated antigens c: serial number of main classes of mutated antigens cc: serial number of subclasses of mutated antigens V: spectral vector of mutated antigens W: central spectral vector of mutated antigens σ : recognition radius of mutated antigens m: number of recognized mutated antigens

6.3 Classification of Detection Images Based on Artificial Immune Network

263

cluster AB has its corresponding recognition radius σ . In the section, ABc is used to represent a cluster of antibodies ab.c that are all in a main class with the serial number of c in the artificial immune system and c ∈ C = {1, 2, . . . ,{n c }; n c is the total number } of classes of antibodies. In an antibody cluster AB = AB1 ∪ AB2 ∪ · · · ABn c , each antibody ab recognizes antigens ag within its recognition radius. Meanwhile, to reduce the number of antibodies ab, the antibodies retained after training should recognize as many antigens ag as possible, so that the antibody cluster AB finally covers the entire feature space of antigens. To distinguish whether an antigen has been recognized or not and avoid repeated recognition, the antigen recognition tag ag.r is introduced to the antigen model: { ag.r = f (ag.V ,ab.W ) =

1 T = K (ag.V ,ab.W ) − ab.σ > 0 0 otherwise

(6.19)

where K is a kernel function; K (ag.V , ab.W ) − σ > 0 indicates that an antigen ag is within the recognition radius of an antibody ab; that is, the antigen ag can be recognized by the antibody ab; correspondingly, the antigen recognition tag is ag.r = 1; otherwise, it is ag.r = 0.

Kernel Space Projection According to the necessity analysis of nonlinear kernel space projection of AIN, the kernel function K (x, y) is used to replace inner product operation x T y, which realizes kernel space projection of the traditional AIN model. Based on the basic principle of SVM in data processing and the method similar to generalized linear discrimination function, the spectra of antigens are projected to a high-dimensional space through the kernel function K (x, y) to compare similarity of spectra of antigens in the high-dimensional space. This improves the method of training antibodies and classifying and recognizing antibodies in AIN and overcomes the problem of linear indivisibility incurred by nonlinear spectra. At present, the commonly used kernel functions mainly include: (1) Polynomial kernel function K (x, y) = (x T y + 1)d

(6.20)

where d denotes the order of the polynomial kernel function. (2) Radial basis function ) ( ||x − y||2 K r (x, y) = exp − r2 where r represents the control radius.

(6.21)

264

6 Detection and Processing of Synthetic Attributes of Integrated …

(3) Kernel function of neural network K (x, y) =

1 1 + exp(vx T y − a)

(6.22)

where v and a are both constants.

6.3.2 AIN Training and Classification of Detection Images The recognition process of antibodies for various classes of antigens is an important link in AIN training and target classification. The recognition process is briefly described combining Figs. 6.29 and 6.30. Figure 6.29 { shows the initial antigen } AG = ag|agnum = 1, 2, . . . , 6 and distribution in a 2D feature space, in which 1 { } AG2 = ag|agnum = 7, 8, . . . , 11 separately belong to the first and second classes of antigens. It is assumed that an antibody ab11 and its recognition radius σ1 are obtained through training at first. The first and second subscripts of the antibody ab11 separately represent serial numbers of the main class and subclass. ab11 recognizes a same class of antigens ag1 , ag2 , ag3 , and ag4 , and then the recognition tag r of ag1 . . . ag4 is modified as 1, which does not take part in subsequent training. After multiple times of training, all antigens ag1 . . . ag11 are recognized by the produced antibodies. After training, antibodies ab11 , ab12 , ab21 , and ab22 are obtained (Fig. 6.30), among which ab11 and ab12 belong to an antibody cluster AB1 , while ab21 and ab22 belong to another antibody cluster AB2 . Target classification using AIN mainly involves three steps: sample selection and AIN initialization, AIN training, and target classification. (1) Sample selection and AIN initialization Several regions with known classes in hyperspectral images are defined as regions of interest (ROIs) to form antigens AG of training samples, in which ag is a single Fig. 6.29 Initial sample distribution of antigens

6.3 Classification of Detection Images Based on Artificial Immune Network

265

Fig. 6.30 Distribution of antigens and antibodies after recognition of antigens

antigen and ag ∈ AG. Antigens in the same class are labeled as ag.c ≡ c ∈ C = {1, 2, . . . , n c }, in which n c denotes the total number of classes of antigens contained in a ROI. All antigens ag.V are normalized spectrally and their recognition tags ag.r are set to be 0. Because no antibody is generated during initialization, the total number of classes of antibodies is n c = 0. (2) AIN training After antigen selection and AIN initialization, AIN produces corresponding antibodies by training antigens. For antigens AGc in any class c, the following six training steps are cycled until the produced antibodies ABc can recognize all antigens AGc . The specific flowchart is illustrated in Fig. 6.31. Step 1: Selection. agp is selected as the primary antigen and it is the antigen with the shortest distance to the central vector Center.V , that is, { } agp = ag|arg min ED(ag.V ,Center.V ), ag.r = 0 (6.23) ag∈AGc

Σn Center.V =

j=1

n

ag.V

(6.24)

where n is the number of antigens in AGc ; ED(·) is the Euclidean distance (ED). Step 2: Cloning. (The primary antigen is clonally { }( replicated to generate the cloned antigen cluster CA CA = ca1 , ca2 , . . . , can m . n m is the total number of antigens in the class of the primary antigen. Step 3: Mutation. Antigens in CA are mutated according to a certain mutation probability }pm , thus generating the mutated antigen cluster MU = { mu1 , mu2 , . . . , mun m . Mutated antigens are important bridges that link antigens and antibodies. In the initial state, the serial number cc of subclass of mutated antigens is 0; the number m of recognized mutated antigens is 0; the central spectrum mu.W of mutated antigens is an empty array; the recognition radius is mu.σ = 0.

266

6 Detection and Processing of Synthetic Attributes of Integrated …

Fig. 6.31 Flowchart of AIN training

spectral vector Mutation is performed following ( Eq. (6.25), { which mainly changes the }( mui .V of mutated antigens mui .V = mui .v1 , mui .v2 , . . . , mui .v Nb , in which mui .vk = cai .vk + pm N (0, 1)(MAXk − MINk )

(6.25)

where MAXk and MINk are the maximum and minimum of the antigen cluster in the kth band; N (0, 1) is a random number following (0,1) Gaussian distribution. The mutation probability pm can be adjusted in the range of (0, 0.3] according to hyperspectral images. When antigens differ significantly in spectra, the mutation probability is large; otherwise, it is small. Step 4: Generation of immune antibodies. After antigen selection, cloning, and mutation, it enters the generation stage of immune antibodies, which are generated following these three steps: Step 4.1: Calculation of the recognition radius. Each mutated antigen mu in the mutated antigen cluster is taken as the candidate antibody. The spectrum mu.V

6.3 Classification of Detection Images Based on Artificial Immune Network

267

of mutated antigens is copied to the central spectrum mu.W of mutated antigens, that is, mui .W = (mui .w1 , mui .w2 , . . . , mui .w Nb )T = (mui .v1 , mui .v2 , . . . , mui .v Nb )T (6.26) The recognition radius mu.σab of mutated antigens is set to be mui .σab = (d1 + d2 )/2

(6.27)

d1 = max {K (mui .W , ag.V )}

(6.28)

d2 = min {K (mui .W , ag.V ) > d1 }

(6.29)

ag∈AGc

ag∈AGc

where K (·) is the kernel function. According to the nature of kernel functions, the closer the distance between mui .W and ag.V is in the spectral space, the greater the value of K (mui .W ,ag.V ). In accordance with the basic principle of immune network, ABc should recognize all antigens that belong to class c in the training antigens, while it cannot recognize any antigen that does not belong to class c. To meet the requirement, antigens that do not belong to the class c while have the highest similarity in the kernel space are selected as points on the outer boundary. d1 is used to represent the distance between antigens on the outermost boundary and the mutated antigens in the kernel space, and it plays a role in defining the outer boundary and preventing antigens in other classes from participating in training. Antigens showing the lowest similarity with the kernel space of mutated antigens are searched in class c to serve as points on the maximum inner boundary of a same class of antigens. The distance d2 in the kernel space is used to determine the maximum inner boundary for recognizing the same class of antigens. The condition d2 > d1 ensures that mutated antigens only recognize the same class of antigens. Using Eqs. (6.28) and (6.29), the fundamental objective of using generated antibodies to recognize as many antigens in the same class as possible can be achieved. Step 4.2: Generation of antibodies. After obtaining the central spectral vectors mu.W and recognition radius mu.σ of each mutated antigen, the mutated antigens are used to recognize the same class of antigens AGc again. The numbers m i of recognized antigens by each mutated antigen are separately recorded and the number m i of recognized antigens by mutated antigens in the mutated antigen structure is changed into mui .m = m i . The mutated antigen with the maximum number mui .m of recognized antigens is selected from all mutated antigens to serve as the newly formed antibody. Then, relevant parameters of the mutated antigen are assigned to the antibody model to form a new antibody ab. The antibody belongs to the subclass ab.cc = ab.cc + 1, and the total number of antibodies in class c is n cB = n cB + 1. Step 4.3: Modification of recognition tags. AGc is recognized again using the newly formed antibody ab and the recognition tags ag.r of antigens are modified using Eqs. (6.30) and (6.31).

268

6 Detection and Processing of Synthetic Attributes of Integrated …

{ ag.r =

1T ≥0 0T (B1 − B0 ) ηD-S < H0

(6.43)

The target detection algorithm by airborne and spaceborne image fusion based on 3D GMRF and D-S evidence theory can improve the detection probability and false alarm probability of the traditional target detection algorithms only based on spaceborne or airborne remote-sensing images. The basic procedures of the algorithm are illustrated in Fig. 6.55.

296

6 Detection and Processing of Synthetic Attributes of Integrated …

Fig. 6.55 Basic procedures of the target detection algorithm by airborne and spaceborne image fusion based on 3D GMRF and D-S evidence theory

In Fig. 6.55, the airborne and spaceborne remote-sensing images used for the target detection technique by airborne and spaceborne image fusion can be taken by different platforms or different sensors. This enriches image information input in the whole detection system and is more favorable for target detection and identification [23]. However, because it is difficult to attain airborne and spaceborne images in the same time phase and same place at the same time, a corresponding spaceborne remote-sensing image can be obtained via modeling and simulation of airborne and spaceborne image fusion based on a known airborne remote-sensing image. Then, the two images can be adopted for target detection by airborne and spaceborne image fusion at the same time. In the airborne/spaceborne D-S evidence fusion model, the target identification probability Pri of airborne and spaceborne images by a single 3D GMRF detector can be valued according to the subjective experience or the detection precision of the algorithm. In the image for detection results using the 3D GMRF detector, values of various pixels are the statistical distance of GMRF. The statistical distance is normalized into decision reliabilities Bi1 and Bi0 when there are targets or no targets at each pixel, thus finishing valuing of each initial parameter in the D-S evidence fusion model.

6.5.3 Simulation Experiments Experimental data were remote-sensing images of Sandi ego naval experimental base acquired by AVIRIS, and the spaceborne remote-sensing images were attained by simulation and modeling through airborne/spaceborne transformations [24, 25]. The experiments were aimed to detect airplane targets in the images, find airplane targets in the images, and determine locations and coordinates of airplane targets. Therefore, areas with defined distribution of airplane targets were only detected in the experiments. The areas within the black polygons in Fig. 6.56a are the detection region of the algorithm, in which the little polygons represent airplane samples to be detected. After binary calibration, the distribution of the entire detection region is shown as Fig. 6.56b. The distribution of airplane sample points after binary calibration

6.5 Synthetic Attribute Detection of Targets in Sea Background

297

is displayed in Fig. 6.56c, which contains 4522 background pixels and 190 small airplane samples. Detection results of airborne and spaceborne remote-sensing images using the 3D GMRF detector were subjected to decision fusion adopting the D-S evidence theory according to basic methods and procedures of the target detection algorithm by airborne and spaceborne image fusion. This aimed to reach the goal of precision detection of airplane targets. The identification probabilities of the GMRF algorithm based on airborne and spaceborne images were assumed to be Pr 1 = 0.97 and Pr 2 = 0.90. During D-S evidence fusion during target detection based on airborne and spaceborne images, the normalized GMRF statistical distances were used as the decision reliabilities Bi1 and Bi0 with and without targets. Because the images for GMRF statistical distances of airborne and spaceborne images have certain scale difference and interference of background noises, the images for GMRF statistical distances need to be corrected correspondingly. (1) Correction of scale difference The GMRF statistical distances of airborne and spaceborne remote-sensing images obtained using the GMRF detection algorithm are illustrated in Figs. 6.57 and 6.58, respectively. The scales of images for GMRF statistical distances of spaceborne and airborne remote-sensing images are separately 25 × 35 and 70 × 100. To ensure oneto-one correspondence at each pixel in the two images, the image for GMRF statistical distance of spaceborne remote-sensing images was corrected through bicubic interpolation, to magnify the scale to 70 × 100. This aimed to achieve decision reliability registration of various pixels before D-S evidence fusion. The corrected image for GMRF statistical distance is shown as Fig. 6.59. (2) Correction of background noises It can be seen from Fig. 6.59 that background noises bring lots of interference to the non-detection areas at the lower left corner and upper right corner of the image for the GMRF detection results based on spaceborne remote-sensing images. These areas are shown as high protruding peaks in the corresponding image for GMRF statistical distance. The protruding peaks in these areas are mainly caused by the

(a) Distribution of the detection (b) Distribution of the detection (c) Distribution of airplane region and airplane samples region after binary calibration samples after binary calibration

Fig. 6.56 Distribution of detected targets

298

6 Detection and Processing of Synthetic Attributes of Integrated …

Fig. 6.57 GMRF statistical distance of airborne remote-sensing images

Fig. 6.58 GMRF statistical distance of spaceborne remote-sensing images

Fig. 6.59 GMRF statistical distance of spaceborne remote-sensing images after bicubic interpolation

fixed background window of the detection algorithm, and some areas are in the nondetection areas of the remote-sensing images. Therefore, to reduce influences of such background noises on the target detection precision, the GMRF statistical distance in this part is uniformly set to be 0. The GMRF statistical distance after eliminating background noises is shown in Fig. 6.60.

6.5 Synthetic Attribute Detection of Targets in Sea Background

299

Fig. 6.60 GMRF statistical distance after eliminating background noises in the non-detection areas

By using the D-S evidence fusion method in the above target detection algorithm by airborne and spaceborne image fusion, the decision reliabilities of the target detection algorithm by fusion of airborne and spaceborne remote-sensing images with and without targets are calculated to be B1 and B0 . The corresponding basic probability distribution is illustrated in Figs. 6.61 and 6.62. The target detection results by airborne and spaceborne image fusion based on 3D GMRF and D-S evidence theory are obtained by setting different detection thresholds. To verify effectiveness of the algorithm, receiver operating characteristic (ROC) curves were used to carry out comparative experiments on the proposed algorithm with three classical target detection algorithms, namely RX algorithm, adaptive matched filter (AMF), and orthogonal subspace projection (OSP). ROC curves for target detection are shown in Fig. 6.63. It can be seen from Fig. 6.63 that ROC curves of the target detection algorithm by airborne and spaceborne image fusion after D-S evidence fusion are concentrated in the upper left corner. This indicates that the overall performance of the algorithm is superior to the GMRF target detection algorithm based on airborne or spaceborne remote-sensing images alone. Figure 6.64 shows the detection results of airplane sample points using the target detection algorithm by airborne and spaceborne image Fig. 6.61 Basic probability when there are targets ( H1 ) after D-S evidence fusion

300

6 Detection and Processing of Synthetic Attributes of Integrated …

Fig. 6.62 Basic probability when there are no targets ( H1 ) after D-S evidence fusion

Fig. 6.63 ROC curves of the target detection algorithm by airborne and spaceborne image fusion based on GMRF and D-S evidence theory

Fig. 6.64 Detection results of the target detection algorithm by airborne and spaceborne image fusion based on GMRF and D-S evidence theory (detection probability: 0.935; false alarm probability: 0.037)

fusion based on GMRF and D-S evidence theory under conditions with the detection probability of 0.935 and false alarm probability of 0.037. Theoretical analysis and experiments on the target detection algorithm by airborne and spaceborne image fusion conclude that: the target detection algorithm by airborne and spaceborne image fusion based on 3D GMRF and D-S evidence theory is superior

References

301

to other algorithms in terms of the overall target detection performance. This algorithm uses target detection results based on airborne and spaceborne remote-sensing images for decision fusion in the unit of pixels, which achieves complementation of advantages of airborne and spaceborne remote-sensing images and precise target detection. It can be seen from ROC curves of target detection algorithms that the target detection algorithm by airborne and spaceborne image fusion has a higher target detection probability at the same false alarm rate. Among various target detection algorithms compared, the ROC curves of the target detection algorithm by airborne and spaceborne image fusion are distributed uppermost and left-most, indicative of superiority of the algorithm to other algorithms on the whole. The detection results also suggest that the detection results of the target detection algorithm by airborne and spaceborne image fusion are approximate to actual distribution of airplane targets. The target detection algorithm is more accurate than the 3D GMRF target detection algorithms only based on spaceborne or airborne images.

References 1. Koenderink JJ. The structure of images. Biol Cybern. 1984;50:363–70. 2. Lindeberg T. Scale-space theory: a basic tool for analyzing structures at different scales. J Appl Stat. 1994;21(2):224–70. 3. Chen B, Zhao YG, Li X. A terminal guidance target tracking method based on Gaussian scale-space. J Mil Eng. 2009;30(5):561–6. 4. Luo JH, Huang WJ, Gu XH, et al. Remote sensing monitoring of winter wheat stripe rust based on sensitive band combination of PHI images. Spectrosc Spectral Anal. 2010;30(1):184–7. 5. Chen SJ, Hu YH, Sun DJ, et al. Simulation method for sky integrated fusion based on hyperspectral/multispectral images. J Phys. 2013;62(20):2042011–8. 6. Ritter GX, Urcid G. A lattice matrix method for hyperspectral image unmixing. Inf Sci. 2011;181(10):1787–803. 7. Wang LG, Deng LQ. Hyperspectral imagery classification aiming at protecting classes of interest. In: 2009 WRI World congress on computer science and information engineering (CSIE), vol. 7; 2009. p. 144–7. 8. Mathieu F, Benediktsson JA, Chanussot J, et al. Spectral and spatial classification of hyperspectral data using SVM and morphological profiles. IEEE Trans Geosci Remote Sens. 2008;46(11):3804–14. 9. Zhou S. Research on the application of ant colony algorithm in dimension reduction and classification of hyperspectral images. Harbin: Harbin Institute of Technology; 2010. p. 32–4. 10. Zhang B, Sun X, Gao LR, et al. Endmember extraction of hyperspectral remote sensing images based on the ant colony optimization algorithm. IEEE Trans Geosci Remote Sens. 2011;49(7):2635–46. 11. Zhang Y, Zhang JP, Jin M, et al. Adaptive subspace decomposition and classification for hyperspectral images. Chin J Electron. 2000;9(1):8288–96. 12. Chen SJ, Hu YH, Shi L, et al. Hyperspectral image classification based on space spectrum two-dimensional ant colony combinatorial optimization SVM. Spectro Spectral Anal. 2013;33(8):2192–7. 13. Peng HC, Long F, Ding C. Feature selection based on mutual information criteria of maxdependency. IEEE Trans Pattern Anal Mach Intell. 2005;27(8):1226–38. 14. Yin JH, Wang YS. A fast method for selecting the best spectral segment in hyperspectral remote sensing images. Remote Sens Inf. 2010;3:3–6.

302

6 Detection and Processing of Synthetic Attributes of Integrated …

15. Jerne NK. The immune system. Sci Am. 1973;229(1):52–60. 16. Chen SJ, Hu YH, Sun DJ, et al. Hyperspectral remote sensing image classification based on nonlinear kernel space artificial immune network. J Infrared Millimeter Waves. 2014;33(3):289–96. 17. Chen SJ, Hu YH, Xu SL, Li L, et al. The k-nearest-neighbor simplex based on adaptive C-mutual proportion standard deviation metric for target clustering of hyperspectral remote sensing imagery. J Appl Remote Sens. 2014;8(1). 083578-1-083578-18 18. Hunt BR, Cannon TM. Nonstationary assumptions for Gaussian models of images. IEEE Trans Syst Man Cybern. 1976;12(6):876–82. 19. Wang SB. Research on singular target detection technology in hyperspectral images. Harbin: Harbin Institute of Technology; 2010. 20. Moura JMF, Balram N. Recursive structure of noncausal Gauss-Markov random fields. IEEE Trans Inf Theory. 1992;38(2):334–54. 21. Yang SB, Wang XX. The conflict evidence synthesis method of D-S evidence theory. Beijing: National Defense Industry Press; 2010. 22. Sun JX. Modern pattern recognition. Changsha: National University of Defense Science and Technology Press; 2002. 23. Chen SJ. Research on * * * * technology based on aerospace hyperspectral images. Hefei: PLA Institute of Electronic Engineering; 2011. 24. Gu YL, Zhang DY, Yi WN, et al. Simulation of aerospace optical remote sensor imaging based on aerial images. J Syst Simul. 2008;20(14):3730–2. 25. Xiao Q, Li M. Research on the conversion method from airborne optical images to satellite optical images. J Beijing Normal Univ (Nat Sci Ed). 2007;43(3):234–40.