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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.
Spaceborne Synthetic Aperture Radar (SAR) can observe the ocean surface with high spatial resolution and wide swath under all-weather conditions, day and night. Thus, it is a crucial microwave sensor for obtaining information on sea surface wind and wave fields. This paper reviews various geophysical model functions for wind and wave retrieval and SAR applications in studies of marine atmospheric boundary layer phenomena, offshore wind energy resource development, typhoon monitoring/forecast. The use of traditional SAR and new types of interferometric and polarized SAR data in ocean research are discussed. With the advance of radar satellite technology, the constellation of SAR satellites has become a new trend in the global ocean observations. Many SAR research algorithms have become mature enough to be implemented operationally to provide sea surface wind and wave fields to the scientific communities for ocean dynamic environment monitoring. Spaceborne Synthetic Aperture Radar (SAR) can observe the ocean surface with high spatial resolution and wide swath under all-weather conditions, day and night. Thus, it is a crucial microwave sensor for obtaining information on sea surface wind and wave fields. This paper reviews various geophysical model functions for wind and wave retrieval and SAR applications in studies of marine atmospheric boundary layer phenomena, offshore wind energy resource development, typhoon monitoring/forecast. The use of traditional SAR and new types of interferometric and polarized SAR data in ocean research are discussed. With the advance of radar satellite technology, the constellation of SAR satellites has become a new trend in the global ocean observations. Many SAR research algorithms have become mature enough to be implemented operationally to provide sea surface wind and wave fields to the scientific communities for ocean dynamic environment monitoring.
In this study, a weakly supervised classification method is proposed to classify the Polarimetric Synthetic Aperture Radar (PolSAR) images based on sample refinement using a Complex-Valued Convolutional Neural Network (CV-CNN) to solve the problem that the bounding-box labeled samples contain many heterogeneous components. First, CV-CNN is used for iteratively refining the bounding-box labeled samples, and the CV-CNN that can be used for direct classification is trained simultaneously. Then, the given PolSAR image is classified using the trained CV-CNN. The experimental results obtained using three actual PolSAR images demonstrate that the heterogeneous components can be effectively eliminated using the proposed method, obtaining significantly better classification results when compared with those obtained using the traditional fully supervised classification method in which original bounding-box labeled samples are used. Furthermore, the proposed method with CV-CNN is superior to those in which the classical Support Vector Machine(SVM) and Wishart classifier are used. In this study, a weakly supervised classification method is proposed to classify the Polarimetric Synthetic Aperture Radar (PolSAR) images based on sample refinement using a Complex-Valued Convolutional Neural Network (CV-CNN) to solve the problem that the bounding-box labeled samples contain many heterogeneous components. First, CV-CNN is used for iteratively refining the bounding-box labeled samples, and the CV-CNN that can be used for direct classification is trained simultaneously. Then, the given PolSAR image is classified using the trained CV-CNN. The experimental results obtained using three actual PolSAR images demonstrate that the heterogeneous components can be effectively eliminated using the proposed method, obtaining significantly better classification results when compared with those obtained using the traditional fully supervised classification method in which original bounding-box labeled samples are used. Furthermore, the proposed method with CV-CNN is superior to those in which the classical Support Vector Machine(SVM) and Wishart classifier are used.
Distributed soft target refers to nonrigid target or a target group with wide distribution range, time-varying spatial distribution, or internal relative motion. This type of target is currently attracting considerable interest in the radar field, and the research on its radar characteristics and sensing technology is a typical interdisciplinary problem. To help the radar technicians better understand the related technologies, this study introduces the dynamics, scattering/transmission, radar characteristics, detection, and parameter retrieval of this type of target in continuous and discrete forms, as regards the positive and inverse problems. Considering the aircraft wake vortex as an example, the radar characteristics and sensing technology of this type of target are illustrated, which can serve as a good reference for the development of related radar detection technologies. Distributed soft target refers to nonrigid target or a target group with wide distribution range, time-varying spatial distribution, or internal relative motion. This type of target is currently attracting considerable interest in the radar field, and the research on its radar characteristics and sensing technology is a typical interdisciplinary problem. To help the radar technicians better understand the related technologies, this study introduces the dynamics, scattering/transmission, radar characteristics, detection, and parameter retrieval of this type of target in continuous and discrete forms, as regards the positive and inverse problems. Considering the aircraft wake vortex as an example, the radar characteristics and sensing technology of this type of target are illustrated, which can serve as a good reference for the development of related radar detection technologies.
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.
Building damage assessment is important in disaster emergency monitoring. In recent years, with the increase of multi-polarization capability of Synthetic Aperture Radar (SAR), Polarimetric Synthetic Aperture Radar (PolSAR) provides more possibilities for building damage assessment, and the polarization-characteristic-based building damage assessment method has gradually become the focus of research. However, because of the limitations of data acquisition in PolSAR, current research mainly focuses on the L, C, X, and other limited bands. To obtain an in depth understanding of the polarization characteristics of damaged buildings in SAR images and develop the application of the polarization characteristics of damaged buildings to other bands, this study conducted a simulation experiment of Ku band polarized SAR of buildings, and performed damage assessment feature analysis using the SAR image polarization decomposition method. In this study, a scale model of real materials was built and the “microwave characteristic measurement and simulation imaging scientific experiment platform” was used to conduct SAR simulation imaging of the target buildings. The Ku band polarized SAR images before and after building damage were obtained. Then, the polarization scattering characteristics of buildings before and after damage were analyzed using various common polarization decomposition methods such as \begin{document}$ {H/A/\alpha} $\end{document} decomposition, Yamaguchi decomposition and Touzi decomposition. Results show that the disoriented volume scattering component and the proportion of the disoriented secondary scattering component obtained by the Yamaguchi decomposition and the \begin{document}${ {\alpha }_{\rm s1}} $\end{document} component obtained by the Touzi decomposition have good indicative significance for building damage assessment in the Ku band. Compared with the X band measurement results, the Ku band is more sensitive to building damage assessment, which has important implications for future radar remote sensing applications. Building damage assessment is important in disaster emergency monitoring. In recent years, with the increase of multi-polarization capability of Synthetic Aperture Radar (SAR), Polarimetric Synthetic Aperture Radar (PolSAR) provides more possibilities for building damage assessment, and the polarization-characteristic-based building damage assessment method has gradually become the focus of research. However, because of the limitations of data acquisition in PolSAR, current research mainly focuses on the L, C, X, and other limited bands. To obtain an in depth understanding of the polarization characteristics of damaged buildings in SAR images and develop the application of the polarization characteristics of damaged buildings to other bands, this study conducted a simulation experiment of Ku band polarized SAR of buildings, and performed damage assessment feature analysis using the SAR image polarization decomposition method. In this study, a scale model of real materials was built and the “microwave characteristic measurement and simulation imaging scientific experiment platform” was used to conduct SAR simulation imaging of the target buildings. The Ku band polarized SAR images before and after building damage were obtained. Then, the polarization scattering characteristics of buildings before and after damage were analyzed using various common polarization decomposition methods such as \begin{document}$ {H/A/\alpha} $\end{document} decomposition, Yamaguchi decomposition and Touzi decomposition. Results show that the disoriented volume scattering component and the proportion of the disoriented secondary scattering component obtained by the Yamaguchi decomposition and the \begin{document}${ {\alpha }_{\rm s1}} $\end{document} component obtained by the Touzi decomposition have good indicative significance for building damage assessment in the Ku band. Compared with the X band measurement results, the Ku band is more sensitive to building damage assessment, which has important implications for future radar remote sensing applications.
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This paper reviews the novel azimuthal multi-angle spaceborne Synthetic Aperture Radar (SAR) technique. First, the development status and trend of SAR satellites are analyzed, and their observation capacities are compared considering different aspects. Furthermore, the novel azimuthal multi-angle observation SAR imaging modes are presented based on the application requirements, and the imaging mechanism is analyzed in detail. Moreover, the advantages of the azimuthal multi-angle observation spaceborne SAR system for obtaining full scattering information, geometry information, and motion information of ground targets are analyzed. Detailed conclusions are provided, and experiment results are presented. Finally, the azimuthal multi-angle observation spaceborne SAR technique is summarized, and its prospects are highlighted. This paper reviews the novel azimuthal multi-angle spaceborne Synthetic Aperture Radar (SAR) technique. First, the development status and trend of SAR satellites are analyzed, and their observation capacities are compared considering different aspects. Furthermore, the novel azimuthal multi-angle observation SAR imaging modes are presented based on the application requirements, and the imaging mechanism is analyzed in detail. Moreover, the advantages of the azimuthal multi-angle observation spaceborne SAR system for obtaining full scattering information, geometry information, and motion information of ground targets are analyzed. Detailed conclusions are provided, and experiment results are presented. Finally, the azimuthal multi-angle observation spaceborne SAR technique is summarized, and its prospects are highlighted.
Circular Synthetic Aperture Radar (CSAR) is a novel imaging mode, which has the advantages of all-directional observation, high spatial resolution, and three-dimensional imaging. With the development of airborne CSAR imaging techniques, it has become one of the effective methods for key point area observation. This paper introduces works on airborne CSAR imaging techniques performed by our research team in recent years, including airborne CSAR imaging mode, spatial resolution evaluation, two-dimensional CSAR imaging, three-dimensional target image reconstruction based on a single CSAR, and three-dimensional holographic SAR imaging. In this paper, experimental results based on raw data acquired using airborne CSAR systems with P and X bands are presented. The obtained research results prove the effectivity and practicability of the airborne CSAR imaging mode. The content of this paper is based on a keynote speech presented by the author at the Fifth Young Scientists Forum of Journal of Radars on August 15, 2019. Circular Synthetic Aperture Radar (CSAR) is a novel imaging mode, which has the advantages of all-directional observation, high spatial resolution, and three-dimensional imaging. With the development of airborne CSAR imaging techniques, it has become one of the effective methods for key point area observation. This paper introduces works on airborne CSAR imaging techniques performed by our research team in recent years, including airborne CSAR imaging mode, spatial resolution evaluation, two-dimensional CSAR imaging, three-dimensional target image reconstruction based on a single CSAR, and three-dimensional holographic SAR imaging. In this paper, experimental results based on raw data acquired using airborne CSAR systems with P and X bands are presented. The obtained research results prove the effectivity and practicability of the airborne CSAR imaging mode. The content of this paper is based on a keynote speech presented by the author at the Fifth Young Scientists Forum of Journal of Radars on August 15, 2019.
In recent years, Orthogonal Frequency Division Multiplexing (OFDM) signal has been widely used in Synthetic Aperture Radar (SAR) imaging research due to its orthogonality and large bandwidth. Compared with the traditional SAR, OFDM SAR has certain advantages in imaging applications because of its unique signal characteristics. Nevertheless, OFDM SAR faces many challenges. In this paper, on the basis of different antenna configurations, the problems and studies of single-antenna OFDM SAR imaging and multi-antenna MIMO OFDM SAR imaging are reviewed. The imaging methods of SAR/MIMO SAR based on OFDM and cyclic prefix OFDM signals are discussed, and some possible future research directions of OFDM SAR are presented. In recent years, Orthogonal Frequency Division Multiplexing (OFDM) signal has been widely used in Synthetic Aperture Radar (SAR) imaging research due to its orthogonality and large bandwidth. Compared with the traditional SAR, OFDM SAR has certain advantages in imaging applications because of its unique signal characteristics. Nevertheless, OFDM SAR faces many challenges. In this paper, on the basis of different antenna configurations, the problems and studies of single-antenna OFDM SAR imaging and multi-antenna MIMO OFDM SAR imaging are reviewed. The imaging methods of SAR/MIMO SAR based on OFDM and cyclic prefix OFDM signals are discussed, and some possible future research directions of OFDM SAR are presented.
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.
Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) is one of the most powerful Earth observation techniques, especially useful for measuring highly detailed ground deformation over large ground areas. Much research has been carried out to apply MT-InSAR to monitor ground and infrastructure deformation in urban areas related to land reclamation, underground construction and groundwater extraction. This paper reviews the progress in the research and identifies challenges in applying the technology, including the inconsistency in coherent point identification when different approaches are used, the reliability issue in parameter estimation, difficulty in accurate geolocation of measured points, the one-dimensional line-of-sight nature of InSAR measurements, the inability of making complete measurements over an area due to geometric distortions, especially the shadowing effects, the challenges in processing large SAR datasets, the decrease of the number of coherent points with the increase of the length of SAR time series, and the difficulty in quality control of MT-InSAR results. Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) is one of the most powerful Earth observation techniques, especially useful for measuring highly detailed ground deformation over large ground areas. Much research has been carried out to apply MT-InSAR to monitor ground and infrastructure deformation in urban areas related to land reclamation, underground construction and groundwater extraction. This paper reviews the progress in the research and identifies challenges in applying the technology, including the inconsistency in coherent point identification when different approaches are used, the reliability issue in parameter estimation, difficulty in accurate geolocation of measured points, the one-dimensional line-of-sight nature of InSAR measurements, the inability of making complete measurements over an area due to geometric distortions, especially the shadowing effects, the challenges in processing large SAR datasets, the decrease of the number of coherent points with the increase of the length of SAR time series, and the difficulty in quality control of MT-InSAR results.
Papers
A low pulse repletion frequency is required and an ambiguous Doppler spectrum should be considered to obtain a high-resolution and wide-swath Synthetic Aperture Radar-Ground Moving Target Indication (SAR-GMTI) system in the azimuth direction. In this study, we have proposed a novel clutter suppression approach, where the Doppler spectrum of the single channel echo is ambiguous, with respect to the space-borne multiple channels in an azimuth high-resolution and wide-swath SAR-GMTI system. Initially, the azimuth deramping operation is utilized to compress the ambiguous Doppler spectrum, where the signal of clutter or the moving target is focused toward only some azimuth Doppler-frequency bins. Further, a covariance matrix corresponding to the clutter is estimated in the azimuth deramping and range compression domain. Subsequently, the matrix eigenvalue decomposition technique is employed to obtain an eigenvector corresponding to the minimum eigenvalue. Herein, we intend to achieve redundant channel freedom to ensure the suppression of clutter. The obtained eigenvector can be considered to be the orthogonality vector of the clutter space, which denotes the orthogonality with respect to the signal space vector. Hence, we adopt the obtained eigenvector to appropriately suppress the clutter. Simultaneously, the signal of the moving target can be appropriately preserved. Finally, some experiments are conducted to validate the proposed clutter suppression approach. A low pulse repletion frequency is required and an ambiguous Doppler spectrum should be considered to obtain a high-resolution and wide-swath Synthetic Aperture Radar-Ground Moving Target Indication (SAR-GMTI) system in the azimuth direction. In this study, we have proposed a novel clutter suppression approach, where the Doppler spectrum of the single channel echo is ambiguous, with respect to the space-borne multiple channels in an azimuth high-resolution and wide-swath SAR-GMTI system. Initially, the azimuth deramping operation is utilized to compress the ambiguous Doppler spectrum, where the signal of clutter or the moving target is focused toward only some azimuth Doppler-frequency bins. Further, a covariance matrix corresponding to the clutter is estimated in the azimuth deramping and range compression domain. Subsequently, the matrix eigenvalue decomposition technique is employed to obtain an eigenvector corresponding to the minimum eigenvalue. Herein, we intend to achieve redundant channel freedom to ensure the suppression of clutter. The obtained eigenvector can be considered to be the orthogonality vector of the clutter space, which denotes the orthogonality with respect to the signal space vector. Hence, we adopt the obtained eigenvector to appropriately suppress the clutter. Simultaneously, the signal of the moving target can be appropriately preserved. Finally, some experiments are conducted to validate the proposed clutter suppression approach.
Multi-aspect SAR is a new SAR mode that has two advantages, i.e., long-term observations and a large synthetic-aperture azimuth angle. Previous studies have reported that these unique advantages enable even single-channel systems to have a relatively strong capability for detecting moving targets, i.e., multi-aspect SAR expands and improves the moving-target-related capabilities of the earlier SAR satellite system without increasing its complexity. As such, multi-aspect SAR-GMTI has become a trending topic for research. After reviewing the recent progress and research basis of multi-aspect SAR-GMTI, in this paper, we present our research on the Gaofen-3 SAR, which includes: moving-target detection methods that use the staring spotlight mode, dual-channels GMTI mode, and the dual-channel spotlight GMTI mode. With the results obtained by this research, we hope to establish a basis for the engineering implementation of current and future spaceborne single-channel SAR-GMTI modes and the design of a future spaceborne multi-aspect SAR mode capable of retrieving time-series and dynamic scene information. Multi-aspect SAR is a new SAR mode that has two advantages, i.e., long-term observations and a large synthetic-aperture azimuth angle. Previous studies have reported that these unique advantages enable even single-channel systems to have a relatively strong capability for detecting moving targets, i.e., multi-aspect SAR expands and improves the moving-target-related capabilities of the earlier SAR satellite system without increasing its complexity. As such, multi-aspect SAR-GMTI has become a trending topic for research. After reviewing the recent progress and research basis of multi-aspect SAR-GMTI, in this paper, we present our research on the Gaofen-3 SAR, which includes: moving-target detection methods that use the staring spotlight mode, dual-channels GMTI mode, and the dual-channel spotlight GMTI mode. With the results obtained by this research, we hope to establish a basis for the engineering implementation of current and future spaceborne single-channel SAR-GMTI modes and the design of a future spaceborne multi-aspect SAR mode capable of retrieving time-series and dynamic scene information.
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.
The special imaging mechanism of the Synthetic Aperture Radar (SAR) causes the sidelobe effect on SAR images. In target detection, the sidelobe effect changes the shapes of strong reflective targets, which results in the problems of localization difficulty and localization error. To solve this problem, this paper proposes a ship detection algorithm based on Spatially Variant Apodization (SVA) and Order Statistic-Constant False Alarm Rate (OS-CFAR). First, the global-CFAR algorithm is used to prescreen the potential target points, which reduces the computational burden of the following steps. Second, the SVA algorithm is modified to improve the speed of sidelobe suppression and applied to the raw complex image data. Then, the nonlinear method OS-CFAR is used to detect the targets on the processed image, and the morphological dilation processing is used to make up for the wrong suppressed points caused by the SVA algorithm. Finally, the GF-3 SAR images are used to test the algorithm and the comparison of the image contrast and detected numbers in the results with SVA and without SVA verifies the effectiveness of the proposed algorithm. The special imaging mechanism of the Synthetic Aperture Radar (SAR) causes the sidelobe effect on SAR images. In target detection, the sidelobe effect changes the shapes of strong reflective targets, which results in the problems of localization difficulty and localization error. To solve this problem, this paper proposes a ship detection algorithm based on Spatially Variant Apodization (SVA) and Order Statistic-Constant False Alarm Rate (OS-CFAR). First, the global-CFAR algorithm is used to prescreen the potential target points, which reduces the computational burden of the following steps. Second, the SVA algorithm is modified to improve the speed of sidelobe suppression and applied to the raw complex image data. Then, the nonlinear method OS-CFAR is used to detect the targets on the processed image, and the morphological dilation processing is used to make up for the wrong suppressed points caused by the SVA algorithm. Finally, the GF-3 SAR images are used to test the algorithm and the comparison of the image contrast and detected numbers in the results with SVA and without SVA verifies the effectiveness of the proposed algorithm.
Compact Polarimetric (CP) mode is a new dual-pol mode introduced in the last decade. The main current CP mode transmits circular polarized waves. Data in the form of Stokes parameters obtained by this mode has rotational invariance. In real engineering applications, transmit distortions in all dual-pol modes, including the CP mode, cannot be directly compensated with external calibration methods. Therefore, it is necessary to analysis the influences caused by transmit distortions. Until now, the Maximum Normalized Error (MNE) parameter has already been proposed by existing researches to analyze polarimetric quality of the Polarimetric SAR (PolSAR) system. This paper has proposed an analysis method to analysis the influence of transmit distortions in polarimetric modes with circular polarimetric wave in transmission, based on the Axial Ratio (AR) parameter of real transmitted wave. Firstly, this paper has analyzed the influence of different transmit distortion sources to AR parameter with simulations. Meanwhile, this part has also demonstrated the influence of same distortion sources to the MNE parameter. Through comparison of this two results, this paper has concluded three advantages of the AR parameter over the MNE parameter. At last, the effectiveness of the proposed evaluation method has been verified using real measured GF-3 distortion data and test data obtained by experimental system, which transmit circular polarized waves. Compact Polarimetric (CP) mode is a new dual-pol mode introduced in the last decade. The main current CP mode transmits circular polarized waves. Data in the form of Stokes parameters obtained by this mode has rotational invariance. In real engineering applications, transmit distortions in all dual-pol modes, including the CP mode, cannot be directly compensated with external calibration methods. Therefore, it is necessary to analysis the influences caused by transmit distortions. Until now, the Maximum Normalized Error (MNE) parameter has already been proposed by existing researches to analyze polarimetric quality of the Polarimetric SAR (PolSAR) system. This paper has proposed an analysis method to analysis the influence of transmit distortions in polarimetric modes with circular polarimetric wave in transmission, based on the Axial Ratio (AR) parameter of real transmitted wave. Firstly, this paper has analyzed the influence of different transmit distortion sources to AR parameter with simulations. Meanwhile, this part has also demonstrated the influence of same distortion sources to the MNE parameter. Through comparison of this two results, this paper has concluded three advantages of the AR parameter over the MNE parameter. At last, the effectiveness of the proposed evaluation method has been verified using real measured GF-3 distortion data and test data obtained by experimental system, which transmit circular polarized waves.
As a frequency-domain algorithm for Synthetic Aperture Radar (SAR) imaging, the Range Migration Algorithm (RMA) can theoretically achieve optimal performance. However, because its Stolt mapping is performed using pixel-by-pixel convolution, the computational efficiency of RMA is inadequate for massive SAR data processing requirements. In this paper, we propose a modified RMA based on the Principle of Chirp Scaling (PCS). First, SAR echo data is divided along the range direction, and the subswath signal is compensated by the second-order range-azimuth coupling term and high-order terms at the reference distance. Then, the nonlinear Stolt mapping is modified to become linear. Finally, Stolt interpolation is realized using PCS to efficiently resample the processed data. Demonstrating both well-focused performance and high computational efficiency, the proposed PCS-RMA employs only fast Fourier transforms and complex vector multiplication operations to achieve modified Stolt mapping. The processing results of several simulation data and X-band-measured airborne SAR data with a pulse bandwidth of 1.2 GHz verify the effectiveness of the proposed algorithm. The proposed algorithm can also be employed for the rapid processing of missile-borne, spaceborne, and drone-borne SAR data. As a frequency-domain algorithm for Synthetic Aperture Radar (SAR) imaging, the Range Migration Algorithm (RMA) can theoretically achieve optimal performance. However, because its Stolt mapping is performed using pixel-by-pixel convolution, the computational efficiency of RMA is inadequate for massive SAR data processing requirements. In this paper, we propose a modified RMA based on the Principle of Chirp Scaling (PCS). First, SAR echo data is divided along the range direction, and the subswath signal is compensated by the second-order range-azimuth coupling term and high-order terms at the reference distance. Then, the nonlinear Stolt mapping is modified to become linear. Finally, Stolt interpolation is realized using PCS to efficiently resample the processed data. Demonstrating both well-focused performance and high computational efficiency, the proposed PCS-RMA employs only fast Fourier transforms and complex vector multiplication operations to achieve modified Stolt mapping. The processing results of several simulation data and X-band-measured airborne SAR data with a pulse bandwidth of 1.2 GHz verify the effectiveness of the proposed algorithm. The proposed algorithm can also be employed for the rapid processing of missile-borne, spaceborne, and drone-borne SAR data.
For Synthetic Aperture Radar (SAR) images, traditional super-resolution methods heavily rely on the artificial design of visual features, and super-reconstruction algorithms based on general Convolutional Neural Network (CNN) have poor fidelity to the target edge contour and weak reconstruction ability to small targets. Aiming at the above problems, in this paper, a Dilated-Resnet CNN (DR-CNN) super-resolution model based on feature reuse, i.e., Feature Reuse Dilated-Resnet CNN (FRDR-CNN), is proposed and perceptual loss is introduced, which accurately realizes four times the semantic super-resolution of SAR images. To increase the receptive field, a DR-CNN structure is used to limit the serious loss of the feature map’s resolution in the model, improving the sensitivity to tiny details. To maximize the utilization of features at different levels, the FRDR-CNN cascades the feature maps of different levels, which greatly improves the efficiency of the feature extraction module and further improves the super-resolution accuracy. With the introduction of the perceptual loss, this method has a superior performance in recovering image texture and edge information. Experimental results of the study show that the FRDR-CNN algorithm is more capable of providing small objects’ super-resolution and more accurate in the visual reconstruction of contour details, compared with traditional algorithms and several popular CNN super-resolution algorithms. Objectively, the Peak Signal to Noise Ratio (PSNR) is 33.5023 dB and Structural Similarity Index (SSIM) is 0.5127, and the Edge Preservation Degreebased on the Ratio Of Average (EPD-ROA) is 0.4243 and 0.4373 in the horizontal and vertical directions, respectively. For Synthetic Aperture Radar (SAR) images, traditional super-resolution methods heavily rely on the artificial design of visual features, and super-reconstruction algorithms based on general Convolutional Neural Network (CNN) have poor fidelity to the target edge contour and weak reconstruction ability to small targets. Aiming at the above problems, in this paper, a Dilated-Resnet CNN (DR-CNN) super-resolution model based on feature reuse, i.e., Feature Reuse Dilated-Resnet CNN (FRDR-CNN), is proposed and perceptual loss is introduced, which accurately realizes four times the semantic super-resolution of SAR images. To increase the receptive field, a DR-CNN structure is used to limit the serious loss of the feature map’s resolution in the model, improving the sensitivity to tiny details. To maximize the utilization of features at different levels, the FRDR-CNN cascades the feature maps of different levels, which greatly improves the efficiency of the feature extraction module and further improves the super-resolution accuracy. With the introduction of the perceptual loss, this method has a superior performance in recovering image texture and edge information. Experimental results of the study show that the FRDR-CNN algorithm is more capable of providing small objects’ super-resolution and more accurate in the visual reconstruction of contour details, compared with traditional algorithms and several popular CNN super-resolution algorithms. Objectively, the Peak Signal to Noise Ratio (PSNR) is 33.5023 dB and Structural Similarity Index (SSIM) is 0.5127, and the Edge Preservation Degreebased on the Ratio Of Average (EPD-ROA) is 0.4243 and 0.4373 in the horizontal and vertical directions, respectively.
The layover and shadow phenomenon is serious in urban areas, where the interferometric phase is complex and disordered and interpretation of an image is difficult. Therefore, it is always a hot and difficult problem for InSAR processing. SAR image simulation can provide data support for the study of image processing and understanding methods. However, most existing SAR image simulation methods for construction areas cannot obtain coherent interferometric SAR image pairs. This article proposes an InSAR simulation method for buildings. It can simulate complex images, interferograms, and the number of layover components of the construction areas. In addition, based on the analysis of the phase variation characteristics of the simulation, a reference determination method for the unwrapped phase in the layover area is proposed. It solves the problem of discontinuity of the interferometric phase in the construction areas, with which the traditional method of unwrapping cannot deal effectively. We compared the simulated results using the actual SAR images and interferometric phase and verified the correctness of our simulation method. Moreover, we carry out phase unwrapping and elevation inversion experiments using the simulated and real images and verified the effectiveness of our phase unwrapping method in applying the InSAR elevation inversion. The layover and shadow phenomenon is serious in urban areas, where the interferometric phase is complex and disordered and interpretation of an image is difficult. Therefore, it is always a hot and difficult problem for InSAR processing. SAR image simulation can provide data support for the study of image processing and understanding methods. However, most existing SAR image simulation methods for construction areas cannot obtain coherent interferometric SAR image pairs. This article proposes an InSAR simulation method for buildings. It can simulate complex images, interferograms, and the number of layover components of the construction areas. In addition, based on the analysis of the phase variation characteristics of the simulation, a reference determination method for the unwrapped phase in the layover area is proposed. It solves the problem of discontinuity of the interferometric phase in the construction areas, with which the traditional method of unwrapping cannot deal effectively. We compared the simulated results using the actual SAR images and interferometric phase and verified the correctness of our simulation method. Moreover, we carry out phase unwrapping and elevation inversion experiments using the simulated and real images and verified the effectiveness of our phase unwrapping method in applying the InSAR elevation inversion.
In our previous studies, we demonstrated the usefulness of TanDEM-X interferometric bistatic mode with single polarization to obtain forest heights for the purposes of large area mapping. A key feature of our approach has been the use of a simplified Random Volume Over Ground (RVOG) model that locally estimates forest height. The model takes TanDEM-X interferometric coherence amplitude as an input and uses an external Digital Surface Model (DSM) to account for local slope variations due to terrain topography in order to achieve accurate forest height estimation. The selection of DSM for use as a local slope reference is essential, as an inaccurate DSM will result in less accurate terrain-correction and forest height estimation. In this paper, we assessed TanDEM-X height estimates associated with scale variations in different DSMs used in the model over a remote sensing supersite in Petawawa, Canada. The DSMs used for assessments and comparisons included ASTER GDEM, ALOS GDSM, airborne DRAPE DSM, Canadian DSM and TanDEM-X DSM. Airborne Laser Scanning (ALS) data were used as reference for terrain slope and forest height comparisons. The results showed that, with the exception of the ASTER GDEM, all DSMs were sufficiently accurate for the simplified RVOG model to provide a satisfactory estimate of stand-level forest height. When compared to the ALS 95th height percentile, the modeled forest heights had R2 values greater than 80% and Root-Mean-Square Errors (RMSE) less than 2 m. For a close similarity in slope estimation with the ALS reference, coverage across Canada and open data access, the 0.75 arc-second (20 m) resolution Canadian DSM was selected as a preferred choice for the simplified RVOG model to provide TanDEM-X height estimation in Canada. In our previous studies, we demonstrated the usefulness of TanDEM-X interferometric bistatic mode with single polarization to obtain forest heights for the purposes of large area mapping. A key feature of our approach has been the use of a simplified Random Volume Over Ground (RVOG) model that locally estimates forest height. The model takes TanDEM-X interferometric coherence amplitude as an input and uses an external Digital Surface Model (DSM) to account for local slope variations due to terrain topography in order to achieve accurate forest height estimation. The selection of DSM for use as a local slope reference is essential, as an inaccurate DSM will result in less accurate terrain-correction and forest height estimation. In this paper, we assessed TanDEM-X height estimates associated with scale variations in different DSMs used in the model over a remote sensing supersite in Petawawa, Canada. The DSMs used for assessments and comparisons included ASTER GDEM, ALOS GDSM, airborne DRAPE DSM, Canadian DSM and TanDEM-X DSM. Airborne Laser Scanning (ALS) data were used as reference for terrain slope and forest height comparisons. The results showed that, with the exception of the ASTER GDEM, all DSMs were sufficiently accurate for the simplified RVOG model to provide a satisfactory estimate of stand-level forest height. When compared to the ALS 95th height percentile, the modeled forest heights had R2 values greater than 80% and Root-Mean-Square Errors (RMSE) less than 2 m. For a close similarity in slope estimation with the ALS reference, coverage across Canada and open data access, the 0.75 arc-second (20 m) resolution Canadian DSM was selected as a preferred choice for the simplified RVOG model to provide TanDEM-X height estimation in Canada.

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