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1
2020, 9(1): 1-33.
Abstract
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Spaceborne Synthetic Aperture Radar (SAR), which can be mounted on space vehicles to collect information of the entire planet with all-day and all-weather imaging capacity, has been an indispensable device for earth observation. Currently, the technology of our spaceborne SAR has achieved a considerable technological improvement, including the resolution change from meter to submeter, the imaging mode from stripmap to azimuth beam steering like the sliding spotlight, the practical application of the multichannel approach and the conversion of single polarization into full polarization. With the development of SAR techniques, forthcoming SAR will make breakthroughs in SAR architectures, concepts, technologies and modes, for example, high-resolution wide-swath imaging, multistatic SAR, payload miniaturization and intelligence. All of these will extend the observation dimensions and obtain multidimensional data. This study focuses on the forthcoming development of spaceborne SAR.
Spaceborne Synthetic Aperture Radar (SAR), which can be mounted on space vehicles to collect information of the entire planet with all-day and all-weather imaging capacity, has been an indispensable device for earth observation. Currently, the technology of our spaceborne SAR has achieved a considerable technological improvement, including the resolution change from meter to submeter, the imaging mode from stripmap to azimuth beam steering like the sliding spotlight, the practical application of the multichannel approach and the conversion of single polarization into full polarization. With the development of SAR techniques, forthcoming SAR will make breakthroughs in SAR architectures, concepts, technologies and modes, for example, high-resolution wide-swath imaging, multistatic SAR, payload miniaturization and intelligence. All of these will extend the observation dimensions and obtain multidimensional data. This study focuses on the forthcoming development of spaceborne SAR.
2
2020, 9(3): 554-568.
Landslide disasters occur frequently in the western mountainous regions of China and are characterized by high concealment, suddenness, and strong destructiveness. Early identification of potential disaster hazards is the most effective prevention and mitigation measure. The western mountainous areas mostly have a wide range of alpine-canyon terrain, which is hard or even impossible to reach. Moreover, traditional early identification methods, such as manual inspection, are difficult to implement in these areas. As an emerging radar remote-sensing method, Interferometric Synthetic Aperture Radar (InSAR) can efficiently and accurately identify the hidden dangers of landslides. Based on the synthetic aperture radar data of the European Space Agency’s Sentinel-1, this study used time series InSAR technology to identify the potential landslide hazards in the alpine-canyon terrain along the Yajiang-Muli County of the Yalong River; eight potential geohazards were detected. On the basis of the historical data of landslide hazards and the interpretation of optical remote sensing data, the results of early identification were verified and analyzed, and the danger level of the disaster points was evaluated. The influence of geometric distortion in InSAR technology on the early identification of potential landslides in alpine-canyon terrain was also discussed. This case study can provide powerful data and technical support for local disaster prevention and mitigation and provide ideas and references for the early identification of the hidden dangers of landslides in mountain-valley areas.
Landslide disasters occur frequently in the western mountainous regions of China and are characterized by high concealment, suddenness, and strong destructiveness. Early identification of potential disaster hazards is the most effective prevention and mitigation measure. The western mountainous areas mostly have a wide range of alpine-canyon terrain, which is hard or even impossible to reach. Moreover, traditional early identification methods, such as manual inspection, are difficult to implement in these areas. As an emerging radar remote-sensing method, Interferometric Synthetic Aperture Radar (InSAR) can efficiently and accurately identify the hidden dangers of landslides. Based on the synthetic aperture radar data of the European Space Agency’s Sentinel-1, this study used time series InSAR technology to identify the potential landslide hazards in the alpine-canyon terrain along the Yajiang-Muli County of the Yalong River; eight potential geohazards were detected. On the basis of the historical data of landslide hazards and the interpretation of optical remote sensing data, the results of early identification were verified and analyzed, and the danger level of the disaster points was evaluated. The influence of geometric distortion in InSAR technology on the early identification of potential landslides in alpine-canyon terrain was also discussed. This case study can provide powerful data and technical support for local disaster prevention and mitigation and provide ideas and references for the early identification of the hidden dangers of landslides in mountain-valley areas.
3
2020, 9(4): 684-714.
Radar target detection in sea clutter is of significance to both the civil and military applications. With the miniaturization and invisibility of sea targets, Small Floating Targets (SFTs) with slow speed have become the focus of radar detection. However, the detection of SFTs in the background of sea clutter has always been a challenging problem. SFTs usually have a weak Radar Cross Section (RCS) and slow speed, making them difficult to be detected in sea clutter. Traditional target detection methods exhibit poor performance in the detection of SFTs. For the detection of small and weak targets on the sea surface, a high Doppler resolution and high range resolution system (double-high system) is an effective approach to solve this problem. In the double-high system, the target echo received by the radar provides readily available and sufficient information. However, how to transform and refine this information to improve detection performance has always been a challenge to the radar industry. In recent years, as an artificial feature engineering stage for intelligent radar target detection, scholars have proposed various feature-based target detection methods based on the double-high system to alleviate the difficulty of SFT detection when relying only on energy information and to considerably improve the detection performance. To ensure that relevant radar practitioners better understand the development of this field in recent years and the future trend, this paper summarizes the difficulties of sea target detection and common target detection methods, analyzes the principle and general framework of feature detection and several typical feature-based detection methods, and explores the development trend of feature-based detection methods.
Radar target detection in sea clutter is of significance to both the civil and military applications. With the miniaturization and invisibility of sea targets, Small Floating Targets (SFTs) with slow speed have become the focus of radar detection. However, the detection of SFTs in the background of sea clutter has always been a challenging problem. SFTs usually have a weak Radar Cross Section (RCS) and slow speed, making them difficult to be detected in sea clutter. Traditional target detection methods exhibit poor performance in the detection of SFTs. For the detection of small and weak targets on the sea surface, a high Doppler resolution and high range resolution system (double-high system) is an effective approach to solve this problem. In the double-high system, the target echo received by the radar provides readily available and sufficient information. However, how to transform and refine this information to improve detection performance has always been a challenge to the radar industry. In recent years, as an artificial feature engineering stage for intelligent radar target detection, scholars have proposed various feature-based target detection methods based on the double-high system to alleviate the difficulty of SFT detection when relying only on energy information and to considerably improve the detection performance. To ensure that relevant radar practitioners better understand the development of this field in recent years and the future trend, this paper summarizes the difficulties of sea target detection and common target detection methods, analyzes the principle and general framework of feature detection and several typical feature-based detection methods, and explores the development trend of feature-based detection methods.
4
2020, 9(5): 803-827.
Flying birds and Unmanned Aerial Vehicles (UAVs) are typical “low, slow, and small” targets with low observability. The need for effective monitoring and identification of these two targets has become urgent and must be solved to ensure the safety of air routes and urban areas. There are many types of flying birds and UAVs that are characterized by low flying heights, strong maneuverability, small radar cross-sectional areas, and complicated detection environments, which are posing great challenges in target detection worldwide. “Visible (high detection ability) and clear-cut (high recognition probability)” methods and technologies must be developed that can finely describe and recognize UAVs, flying birds, and “low-slow-small” targets. This paper reviews the recent progress in research on detection and recognition technologies for rotor UAVs and flying birds in complex scenes and discusses effective detection and recognition methods for the detection of birds and drones, including echo modeling and recognition of fretting characteristics, the enhancement and extraction of maneuvering features in ubiquitous observation mode, distributed multi-view features fusion, differences in motion trajectories, and intelligent classification via deep learning. Lastly, the problems of existing research approaches are summarized, and we consider the future development prospects of target detection and recognition technologies for flying birds and UAVs in complex scenarios.
Flying birds and Unmanned Aerial Vehicles (UAVs) are typical “low, slow, and small” targets with low observability. The need for effective monitoring and identification of these two targets has become urgent and must be solved to ensure the safety of air routes and urban areas. There are many types of flying birds and UAVs that are characterized by low flying heights, strong maneuverability, small radar cross-sectional areas, and complicated detection environments, which are posing great challenges in target detection worldwide. “Visible (high detection ability) and clear-cut (high recognition probability)” methods and technologies must be developed that can finely describe and recognize UAVs, flying birds, and “low-slow-small” targets. This paper reviews the recent progress in research on detection and recognition technologies for rotor UAVs and flying birds in complex scenes and discusses effective detection and recognition methods for the detection of birds and drones, including echo modeling and recognition of fretting characteristics, the enhancement and extraction of maneuvering features in ubiquitous observation mode, distributed multi-view features fusion, differences in motion trajectories, and intelligent classification via deep learning. Lastly, the problems of existing research approaches are summarized, and we consider the future development prospects of target detection and recognition technologies for flying birds and UAVs in complex scenarios.
5
2020, 9(5): 753-764.
Synthetic Aperture Radar (SAR) is an all-weather and all-time imaging radar with high resolution, which is widely used for enemy reconnaissance to provide timely and accurate intelligence for taking decisions during wars. It has become a hot issue in the contemporary electronic warfare to suppress and disorder the reconnaissance imaging of SAR equipment for protecting high-value targets and important strategic areas. This study discusses the development and future trend of SAR jamming techniques. First, the history of development of SAR jamming techniques is discussed and explained in detail. Then, the advantages and disadvantages of the typical SAR jamming models are comparatively analyzed together with simulation experiments. Finally, the current defects of the SAR jamming techniques are summarized and the future trend of the SAR jamming techniques is also pointed out, providing some reference for experts and scholars.
Synthetic Aperture Radar (SAR) is an all-weather and all-time imaging radar with high resolution, which is widely used for enemy reconnaissance to provide timely and accurate intelligence for taking decisions during wars. It has become a hot issue in the contemporary electronic warfare to suppress and disorder the reconnaissance imaging of SAR equipment for protecting high-value targets and important strategic areas. This study discusses the development and future trend of SAR jamming techniques. First, the history of development of SAR jamming techniques is discussed and explained in detail. Then, the advantages and disadvantages of the typical SAR jamming models are comparatively analyzed together with simulation experiments. Finally, the current defects of the SAR jamming techniques are summarized and the future trend of the SAR jamming techniques is also pointed out, providing some reference for experts and scholars.
6
2020, 9(1): 73-85.
In recent years, spaceborne Interferometric Synthetic Aperture Radar (InSAR) technology has shown increasing application potential in the field of geohazard monitoring. In this article, we first introduce the principle of InSAR technology, then systematically review the development of InSAR technology and analyze the technical characteristics and applicable scope of methods such as differential InSAR and time-series InSAR. We then discuss the application status and development trend of InSAR technology in geohazard monitoring with respect to earthquakes, landslides, hydropower projects, and ground subsidence. Finally, to guide future work in the dynamic monitoring and prevention of geohazards, we summarize the key issues and scientific problems faced by the application of InSAR to geohazard monitoring, which include atmospheric correction, complex-area deformation data acquisition, and the acquisition of multidimensional deformation data. Judging from the current applications of geological hazard monitoring, this technology is now at the point of extensive application. With the development of future spaceborne SAR satellite systems and the driving force of industry application, InSAR technology will develop into a sophisticated high-precision ground observation technology that will have a huge impact on geological hazard monitoring.
In recent years, spaceborne Interferometric Synthetic Aperture Radar (InSAR) technology has shown increasing application potential in the field of geohazard monitoring. In this article, we first introduce the principle of InSAR technology, then systematically review the development of InSAR technology and analyze the technical characteristics and applicable scope of methods such as differential InSAR and time-series InSAR. We then discuss the application status and development trend of InSAR technology in geohazard monitoring with respect to earthquakes, landslides, hydropower projects, and ground subsidence. Finally, to guide future work in the dynamic monitoring and prevention of geohazards, we summarize the key issues and scientific problems faced by the application of InSAR to geohazard monitoring, which include atmospheric correction, complex-area deformation data acquisition, and the acquisition of multidimensional deformation data. Judging from the current applications of geological hazard monitoring, this technology is now at the point of extensive application. With the development of future spaceborne SAR satellite systems and the driving force of industry application, InSAR technology will develop into a sophisticated high-precision ground observation technology that will have a huge impact on geological hazard monitoring.
7
2020, 9(1): 34-54.
As an active microwave imaging sensor, Synthetic Aperture Radar (SAR) has become one of the main means of Earth observation owing to its unique technical advantages of all-day, all-weather operation and long working distance. As such, it plays a very important role in military and civilian fields. With the development of SAR remote-sensing technology, high-resolution, high-quality SAR images are produced continuously. However, manual detection and recognition of targets of interest is time-consuming and laborious, so the development of Automatic Target Recognition (ATR) technology is a matter of urgency. The typical SAR ATR system primarily comprises three stages: detection, discrimination, and classification/recognition. The detection and discrimination stages are the basis of the SAR ATR system, and research on SAR applications in the radar field has been conducted by researchers around the world. For single-channel SAR images, target detection and discrimination from simple scenes yield good results. However, in complex scenes, the clutter scattering intensity is relatively high, the clutter background is heterogenous, the target scattering intensity is relatively weak, and the target distribution is dense. These factors continue to make accurate SAR target detection and discrimination difficult. In this paper, we summarize the recent research progress on single-channel SAR target detection and discrimination methods for complex scenes, analyze the characteristics and problems associated with various methods, and consider the future development trend of single-channel SAR target detection and discrimination methods for complex scenes.
As an active microwave imaging sensor, Synthetic Aperture Radar (SAR) has become one of the main means of Earth observation owing to its unique technical advantages of all-day, all-weather operation and long working distance. As such, it plays a very important role in military and civilian fields. With the development of SAR remote-sensing technology, high-resolution, high-quality SAR images are produced continuously. However, manual detection and recognition of targets of interest is time-consuming and laborious, so the development of Automatic Target Recognition (ATR) technology is a matter of urgency. The typical SAR ATR system primarily comprises three stages: detection, discrimination, and classification/recognition. The detection and discrimination stages are the basis of the SAR ATR system, and research on SAR applications in the radar field has been conducted by researchers around the world. For single-channel SAR images, target detection and discrimination from simple scenes yield good results. However, in complex scenes, the clutter scattering intensity is relatively high, the clutter background is heterogenous, the target scattering intensity is relatively weak, and the target distribution is dense. These factors continue to make accurate SAR target detection and discrimination difficult. In this paper, we summarize the recent research progress on single-channel SAR target detection and discrimination methods for complex scenes, analyze the characteristics and problems associated with various methods, and consider the future development trend of single-channel SAR target detection and discrimination methods for complex scenes.
8
2020, 9(6): 939-958.
Given the functions and performance advantages of passive radar, this paper first reviews the research history of passive radar for more than 80 years and then examines the research progress of related key technologies, including reference signal reconstruction, multipath clutter suppression, target detection, target tracking, and passive radar imaging. On this basis, the latest research results of typical experimental systems of passive radar abroad (particularly in European countries) are presented in terms of system structures, technical parameters, and performance indices. Then this paper focuses on the Multi-Illuminator-based PAssive Radar (MIPAR) series of Wuhan University in China. The target detection results of MIPAR in different frequency bands (HF/VHF/UHF/L) are given, that show the application potential of the MIPAR system in long-range early warning and close-range high-precision monitoring. Finally, the development trends of passive radar, including the integration of multiple illuminators, system network configuration, and intelligent signal processing, are discussed.
Given the functions and performance advantages of passive radar, this paper first reviews the research history of passive radar for more than 80 years and then examines the research progress of related key technologies, including reference signal reconstruction, multipath clutter suppression, target detection, target tracking, and passive radar imaging. On this basis, the latest research results of typical experimental systems of passive radar abroad (particularly in European countries) are presented in terms of system structures, technical parameters, and performance indices. Then this paper focuses on the Multi-Illuminator-based PAssive Radar (MIPAR) series of Wuhan University in China. The target detection results of MIPAR in different frequency bands (HF/VHF/UHF/L) are given, that show the application potential of the MIPAR system in long-range early warning and close-range high-precision monitoring. Finally, the development trends of passive radar, including the integration of multiple illuminators, system network configuration, and intelligent signal processing, are discussed.
9
2020, 9(1): 86-106.
Synthetic Aperture Radar (SAR) has attracted much attention in the recent decades owing to its all-weather and high-resolution working mode. As an active radar system, the high-resolution imaging process of SAR systems is affected by different types of strong, complex, and variable electromagnetic interferences that can severely affect the final high-resolution SAR imaging results. Thus, developing ways to effectively suppress complex electromagnetic interferences is a major challenge and focus of SAR detection. In this paper, we summarize the key elements and main concepts underlying interference suppression in high-resolution SAR imaging, including different interference patterns, interference sources, interference scattering mechanisms, radar antenna configurations, and target characteristics. We then consider the essential task of interference suppression algorithms. Recent papers that detail the representative SAR algorithms used to mitigate suppressed and deceptive jamming are introduced and summarized to provide references for future research.
Synthetic Aperture Radar (SAR) has attracted much attention in the recent decades owing to its all-weather and high-resolution working mode. As an active radar system, the high-resolution imaging process of SAR systems is affected by different types of strong, complex, and variable electromagnetic interferences that can severely affect the final high-resolution SAR imaging results. Thus, developing ways to effectively suppress complex electromagnetic interferences is a major challenge and focus of SAR detection. In this paper, we summarize the key elements and main concepts underlying interference suppression in high-resolution SAR imaging, including different interference patterns, interference sources, interference scattering mechanisms, radar antenna configurations, and target characteristics. We then consider the essential task of interference suppression algorithms. Recent papers that detail the representative SAR algorithms used to mitigate suppressed and deceptive jamming are introduced and summarized to provide references for future research.
10
2020, 9(6): 1014-1031.
Specific emitter identification is a technique of extracting the radio frequency fingerprints of the received electromagnetic signal only using external feature measurements to determine the specific emitter that transmits the signal. In recent years, the related theories and practical applications of specific emitter identification have been continuously improved, and research on radio frequency fingerprinting feature extraction methods has made great progress. Based on the domestic and foreign academic achievements, this paper systematically reviews the status quo of the fingerprint feature extraction method of specific emitter identification. In addition, a new feature classification framework is proposed based on the inherent logic of fingerprint feature extraction. The classification framework combines the description characteristics of different radio frequency fingerprinting features and the correlation between them. It divides the existing radio frequency features into two main categories: direct measurement features and dimensionality reduction transform features, which have three levels. Finally, this paper analyzes and explores several potential research directions of fingerprint feature extraction, aiming to benefit the research and application of specific radiation source identification.
Specific emitter identification is a technique of extracting the radio frequency fingerprints of the received electromagnetic signal only using external feature measurements to determine the specific emitter that transmits the signal. In recent years, the related theories and practical applications of specific emitter identification have been continuously improved, and research on radio frequency fingerprinting feature extraction methods has made great progress. Based on the domestic and foreign academic achievements, this paper systematically reviews the status quo of the fingerprint feature extraction method of specific emitter identification. In addition, a new feature classification framework is proposed based on the inherent logic of fingerprint feature extraction. The classification framework combines the description characteristics of different radio frequency fingerprinting features and the correlation between them. It divides the existing radio frequency features into two main categories: direct measurement features and dimensionality reduction transform features, which have three levels. Finally, this paper analyzes and explores several potential research directions of fingerprint feature extraction, aiming to benefit the research and application of specific radiation source identification.
11
2020, 9(2): 259-276.
Polarimetric Synthetic Aperture Radar (SAR), which can acquire fully polarimetric information, is widely used in civilian and military fields, such as earth observation, damage assessment, and reconnaissance. Major Chinese universities, the Chinese Academy of Sciences, the industrial sector, and user units have conducted research in this field and obtained numerous remarkable achievements. This work reviews the recent progress of research in the field of polarimetric SAR imaging interpretation and recognition. For target scattering interpretation, theories of polarimetric target decomposition and polarimetric rotation domain interpretation are introduced. For polarimetric SAR application, the technologies of ship detection, land cover classification, and building damage assessment, which are based on the interpretation tools, are summarized in combination with the authors’ own research. Finally, the future development perspectives of polarimetric SAR interpretation and recognition are briefly discussed.
Polarimetric Synthetic Aperture Radar (SAR), which can acquire fully polarimetric information, is widely used in civilian and military fields, such as earth observation, damage assessment, and reconnaissance. Major Chinese universities, the Chinese Academy of Sciences, the industrial sector, and user units have conducted research in this field and obtained numerous remarkable achievements. This work reviews the recent progress of research in the field of polarimetric SAR imaging interpretation and recognition. For target scattering interpretation, theories of polarimetric target decomposition and polarimetric rotation domain interpretation are introduced. For polarimetric SAR application, the technologies of ship detection, land cover classification, and building damage assessment, which are based on the interpretation tools, are summarized in combination with the authors’ own research. Finally, the future development perspectives of polarimetric SAR interpretation and recognition are briefly discussed.
12
2020, 9(5): 765-802.
At present, the emphasis of Inverse Synthetic Aperture Radar (ISAR) systems on the characteristics of high carrier frequency, wide bandwidth, multi-polarization capability, distribution, and networking has led to the development and progress of ISAR imaging technology. The development and changes of ISAR imaging technology can be summarized into two aspects: fine imaging to improve the image quality and multidimensional imaging to enrich the image information. The methods of radar fine imaging (such as radar echo pulse compression, radar system distortion correction, high velocity motion compensation, range profile focusing, translational motion compensation, rotational motion compensation, image reconstruction, and image display) are reviewed firstly in this study. Next, the expansion of radar imaging dimensions is summarized, including full polarization fusion, multi-band fusion, multi-station and multi-view imaging, and three-dimensional imaging, etc. Finally, the imaging development trend of combining imaging modeling, fine imaging of complex scene, real-time imaging, image evaluation, and application is proposed.
At present, the emphasis of Inverse Synthetic Aperture Radar (ISAR) systems on the characteristics of high carrier frequency, wide bandwidth, multi-polarization capability, distribution, and networking has led to the development and progress of ISAR imaging technology. The development and changes of ISAR imaging technology can be summarized into two aspects: fine imaging to improve the image quality and multidimensional imaging to enrich the image information. The methods of radar fine imaging (such as radar echo pulse compression, radar system distortion correction, high velocity motion compensation, range profile focusing, translational motion compensation, rotational motion compensation, image reconstruction, and image display) are reviewed firstly in this study. Next, the expansion of radar imaging dimensions is summarized, including full polarization fusion, multi-band fusion, multi-station and multi-view imaging, and three-dimensional imaging, etc. Finally, the imaging development trend of combining imaging modeling, fine imaging of complex scene, real-time imaging, image evaluation, and application is proposed.
13
2020, 9(6): 998-1013.
Passive localization technology, which intercepts emitter signals and passively determines their positions, has important value in fields such as electronic reconnaissance and search and rescue. The traditional passive localization technology approach, i.e., cross-bearing, time difference of arrival, and frequency difference of arrival, requires two steps to estimate the emitter position—estimating the parameters related to the positions and then solving the emitter positions based on the previously estimated parameters. This process results in loss of information and difficulty with data association, and requires high system sensitivity. In recent years, a Direct Position Determination (DPD) method was developed that obtains the emitter positions directly by processing the original sampled signals and requires no estimation of intermediate parameters. This method is robust, achieves high performance with a low signal-to-noise ratio, and requires no parameter association. In this paper, we present a comprehensive summary of existing research on DPD and an overall introduction of DPD, including typical DPD methods based on different information types, DPD of special signals, high-resolution high-accuracy DPD, fast DPD algorithms, and the calibration technology used to address DPD model errors. We also consider the future outlook for DPD.
Passive localization technology, which intercepts emitter signals and passively determines their positions, has important value in fields such as electronic reconnaissance and search and rescue. The traditional passive localization technology approach, i.e., cross-bearing, time difference of arrival, and frequency difference of arrival, requires two steps to estimate the emitter position—estimating the parameters related to the positions and then solving the emitter positions based on the previously estimated parameters. This process results in loss of information and difficulty with data association, and requires high system sensitivity. In recent years, a Direct Position Determination (DPD) method was developed that obtains the emitter positions directly by processing the original sampled signals and requires no estimation of intermediate parameters. This method is robust, achieves high performance with a low signal-to-noise ratio, and requires no parameter association. In this paper, we present a comprehensive summary of existing research on DPD and an overall introduction of DPD, including typical DPD methods based on different information types, DPD of special signals, high-resolution high-accuracy DPD, fast DPD algorithms, and the calibration technology used to address DPD model errors. We also consider the future outlook for DPD.
14
2020, 9(2): 321-334.
Video Synthetic Aperture Radar (SAR) provides dynamic information about an observation scene in a video to the human eye, which can be very useful for the real-time detection of the ground maneuvering targets. The focusing of video SAR data is demanding because of its high data rate. In this study, we discuss suitable focusing algorithms and presents the obtained simulation results. Further, the shadow formation mechanism is analyzed with respect to target detection. Finally, the machine learning algorithm used for detecting the shadows of the moving targets is compared with the classical image processing methods that use real datasets.
Video Synthetic Aperture Radar (SAR) provides dynamic information about an observation scene in a video to the human eye, which can be very useful for the real-time detection of the ground maneuvering targets. The focusing of video SAR data is demanding because of its high data rate. In this study, we discuss suitable focusing algorithms and presents the obtained simulation results. Further, the shadow formation mechanism is analyzed with respect to target detection. Finally, the machine learning algorithm used for detecting the shadows of the moving targets is compared with the classical image processing methods that use real datasets.
15
2020, 9(3): 462-476.
SAR Automatic Target Recognition (ATR) is a key task in microwave remote sensing. Recently, Deep Neural Networks (DNNs) have shown promising results in SAR ATR. However, despite the success of DNNs, their underlying reasoning and decision mechanisms operate essentially like a black box and are unknown to users. This lack of transparency and explainability in SAR ATR pose a severe security risk and reduce the users’ trust in and the verifiability of the decision-making process. To address these challenges, in this paper, we argue that research on the explainability and interpretability of SAR ATR is necessary to enable development of interpretable SAR ATR models and algorithms, and thereby, improve the validity and transparency of AI-based SAR ATR systems. First, we present recent developments in SAR ATR, note current practical challenges, and make a plea for research to improve the explainability and interpretability of SAR ATR. Second, we review and summarize recent research in and practical applications of explainable machine learning and deep learning. Further, we discuss aspects of explainable SAR ATR with respect to model understanding, model diagnosis, and model improvement toward a better understanding of the internal representations and decision mechanisms. Moreover, we emphasize the need to exploit interpretable SAR feature learning and recognition models that integrate SAR physical characteristics and domain knowledge. Finally, we draw our conclusion and suggest future work for SAR ATR that combines data and knowledge-driven methods, human–computer cooperation, and interactive deep learning.
SAR Automatic Target Recognition (ATR) is a key task in microwave remote sensing. Recently, Deep Neural Networks (DNNs) have shown promising results in SAR ATR. However, despite the success of DNNs, their underlying reasoning and decision mechanisms operate essentially like a black box and are unknown to users. This lack of transparency and explainability in SAR ATR pose a severe security risk and reduce the users’ trust in and the verifiability of the decision-making process. To address these challenges, in this paper, we argue that research on the explainability and interpretability of SAR ATR is necessary to enable development of interpretable SAR ATR models and algorithms, and thereby, improve the validity and transparency of AI-based SAR ATR systems. First, we present recent developments in SAR ATR, note current practical challenges, and make a plea for research to improve the explainability and interpretability of SAR ATR. Second, we review and summarize recent research in and practical applications of explainable machine learning and deep learning. Further, we discuss aspects of explainable SAR ATR with respect to model understanding, model diagnosis, and model improvement toward a better understanding of the internal representations and decision mechanisms. Moreover, we emphasize the need to exploit interpretable SAR feature learning and recognition models that integrate SAR physical characteristics and domain knowledge. Finally, we draw our conclusion and suggest future work for SAR ATR that combines data and knowledge-driven methods, human–computer cooperation, and interactive deep learning.
16
2020, 9(3): 444-461.
Active radar remote sensing technology, with its capability of acquiring all-weather data, has great potential for agricultural monitoring. This technology can penetrate vegetation cover more deeply than optical sensors and has sensitivity to the shapes, structures, and dielectric constants of vegetation scatterers. In this paper, we discuss the applications of radar remote sensing in crop identification, cropland soil moisture inversion, crop growth parameter inversion, crop phenology retrieval, agricultural disaster monitoring, and crop yield estimation. We review several specific papers focusing these fields, and then describe the results obtained using information extracted from radar scatterometers and Synthetic Aperture Radar (SAR). Extracted SAR data include characterizations of backscattering, polarimetry, interferometry, and tomography. Lastly, we summarize the problems faced by radar applications in agriculture and consider the future trend of these applications.
Active radar remote sensing technology, with its capability of acquiring all-weather data, has great potential for agricultural monitoring. This technology can penetrate vegetation cover more deeply than optical sensors and has sensitivity to the shapes, structures, and dielectric constants of vegetation scatterers. In this paper, we discuss the applications of radar remote sensing in crop identification, cropland soil moisture inversion, crop growth parameter inversion, crop phenology retrieval, agricultural disaster monitoring, and crop yield estimation. We review several specific papers focusing these fields, and then describe the results obtained using information extracted from radar scatterometers and Synthetic Aperture Radar (SAR). Extracted SAR data include characterizations of backscattering, polarimetry, interferometry, and tomography. Lastly, we summarize the problems faced by radar applications in agriculture and consider the future trend of these applications.
17
2020, 9(4): 654-663.
Owing to the complicated characteristics of high-resolution sea clutter and the diversity of sea-surface small targets, there is no precise parameter model to describe sea clutter and returns with targets. As a result, target detection faces many obstacles. To distinguish sea clutter and target returns, it is effective to extract their features to transform the detection problem into a classification problem in feature space. Feature-based detection is a binary hypothesis test in the feature space that encounters two intrinsic difficulties: one difficulty is insufficient target returns versus sufficient sea clutter; the other difficulty is an uncontrolled false alarm rate in detection. To solve the first difficulty, a generator of typical targets returns that can generate sufficient simulated targets returns is used to balance the number of samples between two classes and assist to design the detector. K Nearest Neighbors (K-NN) is the type of classification method that is simple and effective; however, it cannot be used to detect small targets directly because of the uncontrolled false alarm rate. This paper proposes a modified K-NN method with a controlled false alarm rate for detecting small targets. Experimental results on the IPIX radar database indicate that the proposed detector attains 85.1% and 89.2% rates of target detection for the observation time of 0.512 s and 1.024 s, respectively, compared with other existing feature-based detectors, the proposed detector exhibits 7% and 5% improvement, respectively. Thus, the proposed detector exhibits more stable and effective detection performance than other existing feature-based detectors.
Owing to the complicated characteristics of high-resolution sea clutter and the diversity of sea-surface small targets, there is no precise parameter model to describe sea clutter and returns with targets. As a result, target detection faces many obstacles. To distinguish sea clutter and target returns, it is effective to extract their features to transform the detection problem into a classification problem in feature space. Feature-based detection is a binary hypothesis test in the feature space that encounters two intrinsic difficulties: one difficulty is insufficient target returns versus sufficient sea clutter; the other difficulty is an uncontrolled false alarm rate in detection. To solve the first difficulty, a generator of typical targets returns that can generate sufficient simulated targets returns is used to balance the number of samples between two classes and assist to design the detector. K Nearest Neighbors (K-NN) is the type of classification method that is simple and effective; however, it cannot be used to detect small targets directly because of the uncontrolled false alarm rate. This paper proposes a modified K-NN method with a controlled false alarm rate for detecting small targets. Experimental results on the IPIX radar database indicate that the proposed detector attains 85.1% and 89.2% rates of target detection for the observation time of 0.512 s and 1.024 s, respectively, compared with other existing feature-based detectors, the proposed detector exhibits 7% and 5% improvement, respectively. Thus, the proposed detector exhibits more stable and effective detection performance than other existing feature-based detectors.
18
2020, 9(4): 674-683.
Regarding the target characteristics in marine radar detection, this paper introduces classic radar target characteristics and models, and the main problems associated with the measurement and computation of these target characteristics. From three perspectives, i.e., the target, environment, and sensor, we discuss the target characteristic that have attracted much attention in the field of marine target detection. We discuss the diversity of marine target characteristics, the variety and complexity of the marine environment, coupling effects between the target and the environment, and the main requirements of the typical marine radar in target detection applications. The techniques used in the measurements and computation of target characteristic are also introduced. We propose a multidimensional representation of the target characteristics and briefly discuss its applications.
Regarding the target characteristics in marine radar detection, this paper introduces classic radar target characteristics and models, and the main problems associated with the measurement and computation of these target characteristics. From three perspectives, i.e., the target, environment, and sensor, we discuss the target characteristic that have attracted much attention in the field of marine target detection. We discuss the diversity of marine target characteristics, the variety and complexity of the marine environment, coupling effects between the target and the environment, and the main requirements of the typical marine radar in target detection applications. The techniques used in the measurements and computation of target characteristic are also introduced. We propose a multidimensional representation of the target characteristics and briefly discuss its applications.
19
2020, 9(4): 640-653.
A marine radar device is a major navigation tool for boaters and ships. The images produced by marine radars detect not only hard targets such as ships and coastlines, but also reflections from the sea surface, known as sea clutter. The strong sea clutter and the complex characteristics of marine targets result in transmission of weak echo signals of the images to the radar, which makes difficult for radars to distinguish and analyze. So, effective sea clutter suppression and robust, fast target detection mechanisms are needed for radar to detect marine targets efficiently. However, the existing marine target detection algorithms have limited performance for target detection under complex environments, and have poor adaptability to environment and target characteristics. In this paper, an Integrated Network (INet) for clutter suppression and target detection algorithm is proposed and designed to optimize the signals received from the targets. The layer normalization algorithm integrated with transfer function is used to extract key target features, and the spatial attention network is used to suppress the clutter and to enhance the target signals, and a local cross-scale residual network is built to ensure the weightlessness of the system and accuracy of the detection network. Based on the echo data collected by the navigation radar under various observation conditions, radar images with marine target dataset were constructed. INet was optimized through pre-training of the model and inter-frame accumulation of Plan Position Indicator (PPI) images to obtain the Optimized INet (O-INet). The measured data were verified, tested, and compared with data obtained through various algorithms such as YOLOv3, YOLOv4, two-parameter CFAR, and two-dimensional CA-CFAR. The results obtained prove that the proposed method has superior advantages over other methods in improving detection probability, reducing false alarm rate, and strong generalization ability under complex conditions.
A marine radar device is a major navigation tool for boaters and ships. The images produced by marine radars detect not only hard targets such as ships and coastlines, but also reflections from the sea surface, known as sea clutter. The strong sea clutter and the complex characteristics of marine targets result in transmission of weak echo signals of the images to the radar, which makes difficult for radars to distinguish and analyze. So, effective sea clutter suppression and robust, fast target detection mechanisms are needed for radar to detect marine targets efficiently. However, the existing marine target detection algorithms have limited performance for target detection under complex environments, and have poor adaptability to environment and target characteristics. In this paper, an Integrated Network (INet) for clutter suppression and target detection algorithm is proposed and designed to optimize the signals received from the targets. The layer normalization algorithm integrated with transfer function is used to extract key target features, and the spatial attention network is used to suppress the clutter and to enhance the target signals, and a local cross-scale residual network is built to ensure the weightlessness of the system and accuracy of the detection network. Based on the echo data collected by the navigation radar under various observation conditions, radar images with marine target dataset were constructed. INet was optimized through pre-training of the model and inter-frame accumulation of Plan Position Indicator (PPI) images to obtain the Optimized INet (O-INet). The measured data were verified, tested, and compared with data obtained through various algorithms such as YOLOv3, YOLOv4, two-parameter CFAR, and two-dimensional CA-CFAR. The results obtained prove that the proposed method has superior advantages over other methods in improving detection probability, reducing false alarm rate, and strong generalization ability under complex conditions.
20
2020, 9(3): 497-513.
Target detection and recognition are popular issues in the field of high-resolution Synthetic Aperture Radar (SAR). As a typical target, aircraft detection and identification has certain uniqueness. This paper reviews the development of detection and recognition techniques for a typical target in SAR imagery, analyzes the scattering mechanism and technical difficulties of aircraft in SAR imagery, describes the system flow, technical routes, and key scientific problems of target aircraft detection and recognition in SAR imagery, summarizes the research progress from traditional methods to deep-learning-based methods for aircraft detection and recognition, discusses the characteristics and existing problems of various methods, and predicts the future development trend. This paper proposes that combining target electromagnetic scattering mechanism with deep convolutional neural network to improve the generalization capability of the model is the key to improve SAR detection and recognition performance. Moreover, this paper establishes an aircraft detection method based on the fusion of scattering information and deep convolutional neural network.
Target detection and recognition are popular issues in the field of high-resolution Synthetic Aperture Radar (SAR). As a typical target, aircraft detection and identification has certain uniqueness. This paper reviews the development of detection and recognition techniques for a typical target in SAR imagery, analyzes the scattering mechanism and technical difficulties of aircraft in SAR imagery, describes the system flow, technical routes, and key scientific problems of target aircraft detection and recognition in SAR imagery, summarizes the research progress from traditional methods to deep-learning-based methods for aircraft detection and recognition, discusses the characteristics and existing problems of various methods, and predicts the future development trend. This paper proposes that combining target electromagnetic scattering mechanism with deep convolutional neural network to improve the generalization capability of the model is the key to improve SAR detection and recognition performance. Moreover, this paper establishes an aircraft detection method based on the fusion of scattering information and deep convolutional neural network.
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