Accepted Article Preview
More
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.
Ground-based radar is a microwave remote sensing imaging technology which has been gradually developed and matured in the past 20 years. At present, it has been widely used in the monitoring of geological disasters such as landslide and collapse. Ground-based radars can detect micro-variation in target areas through the principle of interferometry. However, due to human factors, geological factors, meteorological factors, etc., the radar image of the monitoring area is seriously incoherence, which brings great difficulty to long-term quantitative monitoring. Therefore, it is urgent to further develop the application of change detection on the basis of quantitative monitoring, so as to provide effective information for a long-term and comprehensive understanding of the dynamic changes in the monitoring area. To solve the above problems, an unsupervised change detection of ground-based radar images based on an improved Fuzzy C-Means clustering (FCM) algorithm is proposed in this paper. In this method, for the first time, the NonSubsampled Contourlet Transform (NSCT) is performed on the coherence coefficient map and the mean log ratio map to obtain the fusion difference map. Then, Principal Component Analysis (PCA) is used to extract the feature vectors of each pixel in the fusion difference image. The FCM is improved according to the characteristics of ground-based radar images. The improved FCM is used to cluster the feature vectors of each pixel to obtain the change detection result. Ground-based radar LSA was used to monitor the treatment process of a dam in southwest China. During the monitoring process, landslides occurred in the monitoring area affected by precipitation and other factors. This method is used to detect the change of radar image before and after the landslide. The results show that the method in this paper is easier to cluster and segment, and the change detection results can significantly reduce the noise points while retaining the change area. Ground-based radar is a microwave remote sensing imaging technology which has been gradually developed and matured in the past 20 years. At present, it has been widely used in the monitoring of geological disasters such as landslide and collapse. Ground-based radars can detect micro-variation in target areas through the principle of interferometry. However, due to human factors, geological factors, meteorological factors, etc., the radar image of the monitoring area is seriously incoherence, which brings great difficulty to long-term quantitative monitoring. Therefore, it is urgent to further develop the application of change detection on the basis of quantitative monitoring, so as to provide effective information for a long-term and comprehensive understanding of the dynamic changes in the monitoring area. To solve the above problems, an unsupervised change detection of ground-based radar images based on an improved Fuzzy C-Means clustering (FCM) algorithm is proposed in this paper. In this method, for the first time, the NonSubsampled Contourlet Transform (NSCT) is performed on the coherence coefficient map and the mean log ratio map to obtain the fusion difference map. Then, Principal Component Analysis (PCA) is used to extract the feature vectors of each pixel in the fusion difference image. The FCM is improved according to the characteristics of ground-based radar images. The improved FCM is used to cluster the feature vectors of each pixel to obtain the change detection result. Ground-based radar LSA was used to monitor the treatment process of a dam in southwest China. During the monitoring process, landslides occurred in the monitoring area affected by precipitation and other factors. This method is used to detect the change of radar image before and after the landslide. The results show that the method in this paper is easier to cluster and segment, and the change detection results can significantly reduce the noise points while retaining the change area.
The flooded area detection method based on the fusion of optical and Synthetic Aperture Radar (SAR) images is applicable for all weather conditions and times. However, due to the large number of randomly distributed intensive speckle noise in SAR images, the conventional methods of detection often trigger high false alarm rates at flood-stricken zones. Inspired by the Fuzzy C-Means (FCM) clustering method, a hierarchical clustering algorithm (Hierarchical Fuzzy C-Means, H-FCM) is proposed in this paper. This method fuses the SAR image captured after the flood with the optical image captured before the flood. Based on the fused image, this method uses the proposed hierarchical clustering model to obtain the preliminary detection results of the flooded area. Additionally, the algorithm uses the proposed region-growing algorithm to obtain the river location before the flood and uses it as a spatial constraint for the preliminary detection results to further screen out suspected flooded areas and significantly improve detection performance. The experimental data used in this paper include the remote sensing images captured before and after the Gloucester floods in the United Kingdom in 1999, as well as the remote sensing images captured before and after the Nanchang floods in China in 2019. The effectiveness and validity of the H-FCM algorithm are also supported by comparison experiments. The flooded area detection method based on the fusion of optical and Synthetic Aperture Radar (SAR) images is applicable for all weather conditions and times. However, due to the large number of randomly distributed intensive speckle noise in SAR images, the conventional methods of detection often trigger high false alarm rates at flood-stricken zones. Inspired by the Fuzzy C-Means (FCM) clustering method, a hierarchical clustering algorithm (Hierarchical Fuzzy C-Means, H-FCM) is proposed in this paper. This method fuses the SAR image captured after the flood with the optical image captured before the flood. Based on the fused image, this method uses the proposed hierarchical clustering model to obtain the preliminary detection results of the flooded area. Additionally, the algorithm uses the proposed region-growing algorithm to obtain the river location before the flood and uses it as a spatial constraint for the preliminary detection results to further screen out suspected flooded areas and significantly improve detection performance. The experimental data used in this paper include the remote sensing images captured before and after the Gloucester floods in the United Kingdom in 1999, as well as the remote sensing images captured before and after the Nanchang floods in China in 2019. The effectiveness and validity of the H-FCM algorithm are also supported by comparison experiments.
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.
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.
Track initiation is the first important step in group target tracking, and it has a direct effect on the quality of the overall procedure. Traditional radar target tracking methods only utilize information about the target position to detect group numbers, but they do not use information relating to echo amplitude. Tracks are thus easily lost, as the numbers of detected groups and equivalent measurements are inaccurate. This paper proposes a group target track initiation method aided by echo amplitude information to ameliorate these problems. In this respect, target position and echo amplitude information is used to detect the number of target groups, and equivalent measurements are then computed using amplitude weighting and position weighting. Echo amplitude information is employed in the step of detecting group target numbers and computing the equivalent measurements, and group target tracks are subsequently initialized using the modified logic method. The proposed method can be used to correctly detect the number of target groups when the number is previously unknown. Furthermore, the method reduces the rate of track loss and improves the performance of group target tracking. The effectiveness of the proposed method is validated by the simulation results. Track initiation is the first important step in group target tracking, and it has a direct effect on the quality of the overall procedure. Traditional radar target tracking methods only utilize information about the target position to detect group numbers, but they do not use information relating to echo amplitude. Tracks are thus easily lost, as the numbers of detected groups and equivalent measurements are inaccurate. This paper proposes a group target track initiation method aided by echo amplitude information to ameliorate these problems. In this respect, target position and echo amplitude information is used to detect the number of target groups, and equivalent measurements are then computed using amplitude weighting and position weighting. Echo amplitude information is employed in the step of detecting group target numbers and computing the equivalent measurements, and group target tracks are subsequently initialized using the modified logic method. The proposed method can be used to correctly detect the number of target groups when the number is previously unknown. Furthermore, the method reduces the rate of track loss and improves the performance of group target tracking. The effectiveness of the proposed method is validated by the simulation results.
Current Issue
More
Reviews
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.
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.
Polarimetric information enriches the content of a Synthetic Aperture Radar (SAR) and has been widely used in agriculture, environment, ocean, forest, military, and other fields. However, it also faces limitations regarding its low resolution and small width, which lead to high application cost. As a novel polarimetric SAR system that can simultaneously obtain relatively rich scatter information and large swath, Compact Polarimetric SAR (CP SAR) has attracted extensive attention from researchers in the past decade. With the successful launch of India’s RISAT-1 satellite, new progresses have been made in the application fields on CP SAR. In this paper, the classical data processing methods of CP SAR are briefly introduced and the main research results of the application of CP SAR in the agriculture and maritime fields over the past 10 years are summarized. Finally, the prospects on its development are given. Polarimetric information enriches the content of a Synthetic Aperture Radar (SAR) and has been widely used in agriculture, environment, ocean, forest, military, and other fields. However, it also faces limitations regarding its low resolution and small width, which lead to high application cost. As a novel polarimetric SAR system that can simultaneously obtain relatively rich scatter information and large swath, Compact Polarimetric SAR (CP SAR) has attracted extensive attention from researchers in the past decade. With the successful launch of India’s RISAT-1 satellite, new progresses have been made in the application fields on CP SAR. In this paper, the classical data processing methods of CP SAR are briefly introduced and the main research results of the application of CP SAR in the agriculture and maritime fields over the past 10 years are summarized. Finally, the prospects on its development are given.
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.
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.
Papers
One of the most important research fields in Synthetic Aperture Radar (SAR) technology is to improve the accuracies of the number, location, classification, and other parameters of targets of interest. SAR information processing can be mainly divided into two tasks: imaging and interpretation. At present, research efforts on these two tasks are relatively independent. Many algorithms have been developed for SAR imaging and interpretation, and they have become increasingly complex. However, SAR interpretation has not been made simpler by improvements in the imaging resolution. The low recognition rate of key targets, in particular, has yet to be adequately resolved. In this paper, we use a “data driven + intelligence learning” method to improve the information processing ability of airborne SAR based on SAR imaging & interpretation integration. First, we analyze the feasibility and main problems of SAR imaging & interpretation integration using a “data driven + intelligence learning” method. Based on the results, we propose a SAR learning-imaging method based on “data driven + intelligence learning” with the goal of producing an imaging network. The proposed learning-imaging framework, parameter selection method, network training method, and preliminary simulation results are presented, and the key technical problems to be solved are identified and analyzed. One of the most important research fields in Synthetic Aperture Radar (SAR) technology is to improve the accuracies of the number, location, classification, and other parameters of targets of interest. SAR information processing can be mainly divided into two tasks: imaging and interpretation. At present, research efforts on these two tasks are relatively independent. Many algorithms have been developed for SAR imaging and interpretation, and they have become increasingly complex. However, SAR interpretation has not been made simpler by improvements in the imaging resolution. The low recognition rate of key targets, in particular, has yet to be adequately resolved. In this paper, we use a “data driven + intelligence learning” method to improve the information processing ability of airborne SAR based on SAR imaging & interpretation integration. First, we analyze the feasibility and main problems of SAR imaging & interpretation integration using a “data driven + intelligence learning” method. Based on the results, we propose a SAR learning-imaging method based on “data driven + intelligence learning” with the goal of producing an imaging network. The proposed learning-imaging framework, parameter selection method, network training method, and preliminary simulation results are presented, and the key technical problems to be solved are identified and analyzed.
Sparse signal processing-based Synthetic Aperture Radar (SAR) imaging, also known as sparse SAR imaging, is the main research direction of sparse microwave imaging theory. Compared with a conventional SAR system, sparse SAR imaging radar has significant potential to improve imaging performance. However, because it requires heavy computations, the application of sparse SAR imaging in large-scene recovery has become difficult, which restricts its further applications. Additionally, complex SAR images, rather than raw data, are usually used for data archiving due to a number of reasons such as data copyright and system confidentiality. Therefore, it is worthwhile to study how sparse imaging can be achieved using only Matched Filtering (MF) recovered complex images with less computational cost. GaoFen-3 is China’s first 1-m resolution multi-polarization C-band satellite. It has a high-resolution, wide swath imaging ability and hence plays an important role in disaster monitoring and ocean surveillance applications. In this paper, we introduce a complex image-based sparse SAR imaging method to process GaoFen-3 complex image data and improve image performance. Experimental results show that the sparse imaging results have lower sidelobes, higher signal-to-clutter and noise ratio, and better target distinguishing ability compared with inputted images. Additionally, sparse imaging can effectively preserve the statistical distribution and phase information of images that makes the recovered GaoFen-3 sparse image-based applications such as interferometric synthetic aperture radar and constant false alarm ratio detection possible. Sparse signal processing-based Synthetic Aperture Radar (SAR) imaging, also known as sparse SAR imaging, is the main research direction of sparse microwave imaging theory. Compared with a conventional SAR system, sparse SAR imaging radar has significant potential to improve imaging performance. However, because it requires heavy computations, the application of sparse SAR imaging in large-scene recovery has become difficult, which restricts its further applications. Additionally, complex SAR images, rather than raw data, are usually used for data archiving due to a number of reasons such as data copyright and system confidentiality. Therefore, it is worthwhile to study how sparse imaging can be achieved using only Matched Filtering (MF) recovered complex images with less computational cost. GaoFen-3 is China’s first 1-m resolution multi-polarization C-band satellite. It has a high-resolution, wide swath imaging ability and hence plays an important role in disaster monitoring and ocean surveillance applications. In this paper, we introduce a complex image-based sparse SAR imaging method to process GaoFen-3 complex image data and improve image performance. Experimental results show that the sparse imaging results have lower sidelobes, higher signal-to-clutter and noise ratio, and better target distinguishing ability compared with inputted images. Additionally, sparse imaging can effectively preserve the statistical distribution and phase information of images that makes the recovered GaoFen-3 sparse image-based applications such as interferometric synthetic aperture radar and constant false alarm ratio detection possible.
Co-prime-sampling space-borne Synthetic Aperture Radar (SAR) replaces the traditional uniform sampling by performing co-prime sampling in azimuth, which effectively alleviates the conflict between spatial resolution and effective swath width, while also improving the ground detection performance of the SAR system. However, co-prime-sampling in azimuth causes the echo signal to exhibit azimuthal under sampling and non-uniform sampling characteristics, which means the traditional SAR image-processing method can not effectively image co-prime-sampled SAR. In this paper, an imaging method based on Two-Dimensional (2D) sparse-signal reconstruction is proposed for co-prime-sampling space-borne SAR. Using this method, after range-pulse compression, the 2D observed signal is intercepted and a corresponding sparse dictionary consisting of 2D atoms is constructed according to the Doppler parameters of each range gate. Then, azimuth-focus processing is completed by the improved 2D-signal sparsity adaptive matching pursuit algorithm. The proposed method not only compensates for the 2D coupling between the range and azimuth, but also eliminates the influence of space-varying imaging parameters on sparse reconstruction to achieve accurate reconstruction of the entire scene. The simulation results of the point targets and distribution targets verify that the proposed method can effectively reconstruct sparse scenes at a rate much lower than the Nyquist sampling rate. Co-prime-sampling space-borne Synthetic Aperture Radar (SAR) replaces the traditional uniform sampling by performing co-prime sampling in azimuth, which effectively alleviates the conflict between spatial resolution and effective swath width, while also improving the ground detection performance of the SAR system. However, co-prime-sampling in azimuth causes the echo signal to exhibit azimuthal under sampling and non-uniform sampling characteristics, which means the traditional SAR image-processing method can not effectively image co-prime-sampled SAR. In this paper, an imaging method based on Two-Dimensional (2D) sparse-signal reconstruction is proposed for co-prime-sampling space-borne SAR. Using this method, after range-pulse compression, the 2D observed signal is intercepted and a corresponding sparse dictionary consisting of 2D atoms is constructed according to the Doppler parameters of each range gate. Then, azimuth-focus processing is completed by the improved 2D-signal sparsity adaptive matching pursuit algorithm. The proposed method not only compensates for the 2D coupling between the range and azimuth, but also eliminates the influence of space-varying imaging parameters on sparse reconstruction to achieve accurate reconstruction of the entire scene. The simulation results of the point targets and distribution targets verify that the proposed method can effectively reconstruct sparse scenes at a rate much lower than the Nyquist sampling rate.
In the Synthetic Aperture Radar (SAR) remote sensing imagery of complicated scenes (especially urban scenes), there are a large number of lines and surfaces, such as roads in urban areas and the surfaces of buildings. These microwave-signal-scattering features have strong directivity. Traditional SAR acquires the scattering information of a scene from a single observation, and traditional imaging algorithms are based on the point target model, which makes the main features of the lines and surfaces in traditional SAR images appear as a series of strong scattering points rather than line-scattering and surface-scattering features. This outcome ultimately causes the target to be discontinuous in the SAR image, thus making the SAR image difficult to interpret. Therefore, in this study, we conducted an in-depth and meticulous investigation of the SAR imaging mechanism for lines and surfaces by establishing a parametric echo model of typical lines and triangular surfaces. Based on the proposed parametric model, we performed parametric imaging of these lines and surfaces. Based on our results, we propose a parametric imaging method, in which the typical lines and surfaces are classified and determined based on Bayesian theory and the proposed parametric model. Then, an SAR image can be obtained that effectively characterizes the scattering features of the line and surface targets by visual imaging, which effectively facilitates SAR image interpretation. The results of our numerical simulation experiments verify the validity of the proposed method. In the Synthetic Aperture Radar (SAR) remote sensing imagery of complicated scenes (especially urban scenes), there are a large number of lines and surfaces, such as roads in urban areas and the surfaces of buildings. These microwave-signal-scattering features have strong directivity. Traditional SAR acquires the scattering information of a scene from a single observation, and traditional imaging algorithms are based on the point target model, which makes the main features of the lines and surfaces in traditional SAR images appear as a series of strong scattering points rather than line-scattering and surface-scattering features. This outcome ultimately causes the target to be discontinuous in the SAR image, thus making the SAR image difficult to interpret. Therefore, in this study, we conducted an in-depth and meticulous investigation of the SAR imaging mechanism for lines and surfaces by establishing a parametric echo model of typical lines and triangular surfaces. Based on the proposed parametric model, we performed parametric imaging of these lines and surfaces. Based on our results, we propose a parametric imaging method, in which the typical lines and surfaces are classified and determined based on Bayesian theory and the proposed parametric model. Then, an SAR image can be obtained that effectively characterizes the scattering features of the line and surface targets by visual imaging, which effectively facilitates SAR image interpretation. The results of our numerical simulation experiments verify the validity of the proposed method.
An indispensable step in the imaging of Tomographic Synthetic Aperture Radar (TomoSAR) in spectral analysis or Compressive Sensing (CS) technology is estimating the perpendicular baselines in the Perpendicular Line Of Sight (PLOS) in deramping operations. To avoid this procedure, we introduce a Beam Forming (BF) method in the spatial domain that scans for TomoSAR focusing in the PLOS direction. Because of the sophisticated structure of buildings in urban areas, multipass high-resolution SAR images suffer from discrepancies in the look and incidence angles as well as speckle noise. As a result, it is challenging to precisely coregister all the homologous points in the identical pixels of multipass SAR images. To identify the most relevant pixels with respect to both amplitude and phase when BF imaging is implemented, we propose an inconsistency criterion for specific pixels using the joint phase and amplitude of pixels in a window. By minimizing the inconsistency criterion, homologous points with high accuracy can be identified by focusing on TomoSAR imaging. We used simulation and real data from a multipass X-band airborne TomoSAR system in China to test the effectiveness of the proposed method. Experimental results show that the peak of the reflectivity profile via conventional tomographic imaging is about 15.63 m, whereas that by the proposed method is 16.88 m, which is very close to the actual height of the 18 m building. The results demonstrate the feasibility of improving the focusing power of scatterers in the PLOS direction and extracting the three-dimensional outliers of buildings. An indispensable step in the imaging of Tomographic Synthetic Aperture Radar (TomoSAR) in spectral analysis or Compressive Sensing (CS) technology is estimating the perpendicular baselines in the Perpendicular Line Of Sight (PLOS) in deramping operations. To avoid this procedure, we introduce a Beam Forming (BF) method in the spatial domain that scans for TomoSAR focusing in the PLOS direction. Because of the sophisticated structure of buildings in urban areas, multipass high-resolution SAR images suffer from discrepancies in the look and incidence angles as well as speckle noise. As a result, it is challenging to precisely coregister all the homologous points in the identical pixels of multipass SAR images. To identify the most relevant pixels with respect to both amplitude and phase when BF imaging is implemented, we propose an inconsistency criterion for specific pixels using the joint phase and amplitude of pixels in a window. By minimizing the inconsistency criterion, homologous points with high accuracy can be identified by focusing on TomoSAR imaging. We used simulation and real data from a multipass X-band airborne TomoSAR system in China to test the effectiveness of the proposed method. Experimental results show that the peak of the reflectivity profile via conventional tomographic imaging is about 15.63 m, whereas that by the proposed method is 16.88 m, which is very close to the actual height of the 18 m building. The results demonstrate the feasibility of improving the focusing power of scatterers in the PLOS direction and extracting the three-dimensional outliers of buildings.
High-resolution wide-swath SAR moving target imaging is of great significance for target tracking. To achieve target tracking, conversional space-based multichannel SAR technology requires a large number of channels. However, this leads to high system complexity. Moreover, paired false target echoes exist in the azimuth. To address such problems, a high-resolution wide-swath SAR moving target imaging technology based on distributed compressed sensing is proposed in this paper. When the number of channels is large enough, the number of channels is approximately half of that of the conventional high-resolution wide-swath target imaging configuration channel. A distributed compressed sensing observation model is constructed using the sparse property of moving targets and the nonsparse characteristics of the clutter background. Clutter background and sparse moving targets are reconstructed by combining one-dimensional distributed compressed sensing reconstruction in the azimuth and two-dimensional distributed compressed sensing reconstruction in the range-azimuth. Moreover, the paired false target echoes in multichannel SAR moving target are suppressed. The simulation results combined with RADAR-SAT data verify the effectiveness of the proposed technology. High-resolution wide-swath SAR moving target imaging is of great significance for target tracking. To achieve target tracking, conversional space-based multichannel SAR technology requires a large number of channels. However, this leads to high system complexity. Moreover, paired false target echoes exist in the azimuth. To address such problems, a high-resolution wide-swath SAR moving target imaging technology based on distributed compressed sensing is proposed in this paper. When the number of channels is large enough, the number of channels is approximately half of that of the conventional high-resolution wide-swath target imaging configuration channel. A distributed compressed sensing observation model is constructed using the sparse property of moving targets and the nonsparse characteristics of the clutter background. Clutter background and sparse moving targets are reconstructed by combining one-dimensional distributed compressed sensing reconstruction in the azimuth and two-dimensional distributed compressed sensing reconstruction in the range-azimuth. Moreover, the paired false target echoes in multichannel SAR moving target are suppressed. The simulation results combined with RADAR-SAT data verify the effectiveness of the proposed technology.
The extraction of water from Synthetic Aperture Radar (SAR) images is of great significance in water resources investigation and monitoring disasters. To deal with the problems of the insufficient accuracy of water boundaries extracted from middle-low resolution SAR images. This paper proposes a high-precision water boundaries extraction method based on a local super-resolution restoration technology that combines the advantages of the super-resolution restoration technology based on the lightweight residual Convolutional Neural Network (CNN) and the traditional SAR images water extraction methods. The proposed method can significantly improve the accuracy of water segmentation results by using SAR images. To verify the effectiveness of the proposed method, as a study area, we selected the Danjiangkou Reservoir, the water source of the middle route of a south-to-north water diversion project. Further, we conducted experiments on the multi-mode SAR dataset and evaluated its accuracy. This dataset included one Standard Strip-map (SS) mode image obtained by the Chinese GaoFen-3 (GF-3) satellite with a resolution of 8 m and one Interferometric Wide-swath (IW) mode SAR image obtained by Sentinel-1 satellite with a resolution of 20 m. The experimental results showed that the water segmentation results from the middle–low resolution SAR images of the proposed method were more precise, and the overall water segmentation performance was superior to that of the traditional methods. The extraction of water from Synthetic Aperture Radar (SAR) images is of great significance in water resources investigation and monitoring disasters. To deal with the problems of the insufficient accuracy of water boundaries extracted from middle-low resolution SAR images. This paper proposes a high-precision water boundaries extraction method based on a local super-resolution restoration technology that combines the advantages of the super-resolution restoration technology based on the lightweight residual Convolutional Neural Network (CNN) and the traditional SAR images water extraction methods. The proposed method can significantly improve the accuracy of water segmentation results by using SAR images. To verify the effectiveness of the proposed method, as a study area, we selected the Danjiangkou Reservoir, the water source of the middle route of a south-to-north water diversion project. Further, we conducted experiments on the multi-mode SAR dataset and evaluated its accuracy. This dataset included one Standard Strip-map (SS) mode image obtained by the Chinese GaoFen-3 (GF-3) satellite with a resolution of 8 m and one Interferometric Wide-swath (IW) mode SAR image obtained by Sentinel-1 satellite with a resolution of 20 m. The experimental results showed that the water segmentation results from the middle–low resolution SAR images of the proposed method were more precise, and the overall water segmentation performance was superior to that of the traditional methods.
When the direction-of-arrival estimation is made by a single platform, the localization accuracy decreases with increases in the distance from the radiation source, which means the localization accuracy of the azimuth of the radiation source at long distances must be improved. To address this problem, we propose a radiation-source localization algorithm based on a long synthetic aperture. This algorithm introduces the use of synthetic aperture imaging to the passive localization of radiation sources. A long virtual aperture is obtained by movement of the platform, and received data is processed in the form of synthetic aperture radar to obtain high azimuth localization accuracy. For a single-frequency radiation source, the received signal is equivalent to a linear frequency-modulation signal in the azimuth domain, and its chirp rate is inversely proportional to the distance from the radiation source. A range search and azimuth-focusing method are used to perform signal focusing in the range–azimuth domain, and the localization of the radiation source is obtained directly by two-dimensional imaging. Moreover, with respect to the characteristics of wide-beam reconnaissance, we analyzed the range–azimuth coupling relationship of the chirp rate at a low sampling frequency. On this basis, methods for direction-of-arrival estimation and two-dimensional imaging localization are proposed. Our theoretical derivation and experimental data processing results verify the validity of this algorithm. When the direction-of-arrival estimation is made by a single platform, the localization accuracy decreases with increases in the distance from the radiation source, which means the localization accuracy of the azimuth of the radiation source at long distances must be improved. To address this problem, we propose a radiation-source localization algorithm based on a long synthetic aperture. This algorithm introduces the use of synthetic aperture imaging to the passive localization of radiation sources. A long virtual aperture is obtained by movement of the platform, and received data is processed in the form of synthetic aperture radar to obtain high azimuth localization accuracy. For a single-frequency radiation source, the received signal is equivalent to a linear frequency-modulation signal in the azimuth domain, and its chirp rate is inversely proportional to the distance from the radiation source. A range search and azimuth-focusing method are used to perform signal focusing in the range–azimuth domain, and the localization of the radiation source is obtained directly by two-dimensional imaging. Moreover, with respect to the characteristics of wide-beam reconnaissance, we analyzed the range–azimuth coupling relationship of the chirp rate at a low sampling frequency. On this basis, methods for direction-of-arrival estimation and two-dimensional imaging localization are proposed. Our theoretical derivation and experimental data processing results verify the validity of this algorithm.
In recent years, the diffractive optical systems have developed rapidly. Diffractive devices such as binary optical device and membrane-based lens are equivalent to fixed phase shifters of microwave antennas. Thus, the mature theories and methods of a microwave phased-array antenna could be used for diffractive devices’ performance analysis. Both laser Synthetic Aperture Radar (SAR) and laser communication feature a single color and long wavelength, and they are specifically suitable for non-imaging diffractive optical systems. A signal wave front control realized by a diffraction device reduces the focal length and the weight of a system. Research on laser SAR and laser communication technology has important theoretical significance and application value for diffractive optical system. In this paper, we provide a phased-array interpretation of a diffractive optical system and introduce research that has been conducted on airborne and spaceborne laser SAR with respect to diffractive optical systems. We propose the concept of shipborne 1 m diffraction aperture laser communication and an interferometric positioning system and analyze its performance. The results indicated that, using a 10 m short baseline, this system can reach 400 million km with a corresponding positioning accuracy of 6 km that is suitable for use during deep space probes. We also discuss the sparse-sampling laser-imaging problem using a laser to illuminate the target, transforming the laser image signal into the frequency domain with Fourier lens, using the small-scale detector to perform sparse sampling in the low-frequency domain, and reconstructing the target image using a computer. Some preliminary simulation results are provided. In recent years, the diffractive optical systems have developed rapidly. Diffractive devices such as binary optical device and membrane-based lens are equivalent to fixed phase shifters of microwave antennas. Thus, the mature theories and methods of a microwave phased-array antenna could be used for diffractive devices’ performance analysis. Both laser Synthetic Aperture Radar (SAR) and laser communication feature a single color and long wavelength, and they are specifically suitable for non-imaging diffractive optical systems. A signal wave front control realized by a diffraction device reduces the focal length and the weight of a system. Research on laser SAR and laser communication technology has important theoretical significance and application value for diffractive optical system. In this paper, we provide a phased-array interpretation of a diffractive optical system and introduce research that has been conducted on airborne and spaceborne laser SAR with respect to diffractive optical systems. We propose the concept of shipborne 1 m diffraction aperture laser communication and an interferometric positioning system and analyze its performance. The results indicated that, using a 10 m short baseline, this system can reach 400 million km with a corresponding positioning accuracy of 6 km that is suitable for use during deep space probes. We also discuss the sparse-sampling laser-imaging problem using a laser to illuminate the target, transforming the laser image signal into the frequency domain with Fourier lens, using the small-scale detector to perform sparse sampling in the low-frequency domain, and reconstructing the target image using a computer. Some preliminary simulation results are provided.

微信 | 公众平台

随时查询稿件 获取最新论文 知晓行业信息