The method of moving target detection based on subimage cancellation for single-antenna airborne SAR is presented. First the subimage is obtained through frequency processing is pointed out. The imaging difference of ...The method of moving target detection based on subimage cancellation for single-antenna airborne SAR is presented. First the subimage is obtained through frequency processing is pointed out. The imaging difference of a stationary objects and moving object in the subimage based on the frequency division is analyzed from the fundamental principle. Then the developed method combines the shear averaging algorithm to focus on the moving target in the subimage, after the clutter suppression and the focusing position in each subimage is obtained. Next the observation model and the relative movement of the moving targets between the subimages estimate the moving targets. The theoretical analysis and simulation results demonstrate that the method is effective and can not only detect the moving targets, but also estimate their motion parameters precisely.展开更多
The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition...The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.展开更多
For ballistic mid-course targets,in addition to constant orbital motion,the target or any structure on the target undergoes micro-motion dynamics,such as spin,precession and tumbling.The micro-motion characteristics o...For ballistic mid-course targets,in addition to constant orbital motion,the target or any structure on the target undergoes micro-motion dynamics,such as spin,precession and tumbling.The micro-motion characteristics of the ballistic mid-course targets were discussed.The target motion model and inverse synthetic aperture radar(ISAR) imaging model for this kind of targets were built.Then,the influence of micro-motion on ISAR imaging based on the established imaging model was presented.The computer simulation to get mid-course target echoes from static darkroom electromagnetic scattering data based on the established target motion model was realized.The imaging results of computer simulation show the validity of ISAR imaging analysis for micro-motion targets.展开更多
Current research on target detection and recognition from synthetic aperture radar (SAR) images is usually carried out separately. It is difficult to verify the ability of a target recognition algorithm for adapting...Current research on target detection and recognition from synthetic aperture radar (SAR) images is usually carried out separately. It is difficult to verify the ability of a target recognition algorithm for adapting to changes in the environment. To realize the whole process of SAR automatic target recognition (ATR), es- pecially for the detection and recognition of vehicles, an algorithm based on kernel fisher discdminant analysis (KFDA) is proposed. First, in order to make a better description of the difference be- tween the background and the target, KFDA is extended to the detection part. Image samples are obtained with a dual-window approach and features of the inner and outer window samples are extracted by using KFDA. The difference between the features of inner and outer window samples is compared with a threshold to determine whether a vehicle exists. Second, for the target area, we propose an improved KFDA-IMED (image Euclidean distance) combined with a support vector machine (SVM) to recognize the vehicles. Experimental results validate the performance of our method. On the detection task, our proposed method obtains not only a high detection rate but also a low false alarm rate without using any prior information. For the recognition task, our method overcomes the SAR image aspect angle sensitivity, reduces the requirements for image preprocessing and improves the recogni- tion rate.展开更多
The accuracy of background clutter model is a key factor which determines the performance of a constant false alarm rate(CFAR) target detection method. G0 distribution is one of the optimal statistic models in the syn...The accuracy of background clutter model is a key factor which determines the performance of a constant false alarm rate(CFAR) target detection method. G0 distribution is one of the optimal statistic models in the synthetic aperture radar(SAR) image background clutter modeling and can accurately model various complex background clutters in the SAR images. But the application of the distribution is greatly limited by its disadvantages that the parameter estimation is complex and the local detection threshold is difficult to be obtained. In order to solve the above-mentioned problems, an synthetic aperture radar CFAR target detection method using the logarithmic cumulant(Mo LC) + method of moment(Mo M)-based G0 distribution clutter model is proposed. In the method, G0 distribution is used for modeling the background clutters, a new Mo LC+Mo M-based parameter estimation method coupled with a fast iterative algorithm is used for estimating the parameters of G0 distribution and an exquisite dichotomy method is used for obtaining the local detection threshold of CFAR detection, which greatly improves the computational efficiency, detection performance and environmental adaptability of CFAR detection. Experimental results show that the proposed SAR CFAR target detection method has good target detection performance in various complex background clutter environments.展开更多
Not confined to a certain point,such as waveform,this paper systematically studies the low-intercept radio frequency(RF)stealth design of synthetic aperture radar(SAR)from the system level.The study is carried out fro...Not confined to a certain point,such as waveform,this paper systematically studies the low-intercept radio frequency(RF)stealth design of synthetic aperture radar(SAR)from the system level.The study is carried out from two levels.In the first level,the maximum low-intercept range equation of the conventional SAR system is deduced firstly,and then the maximum low-intercept range equation of the multiple-input multiple-output SAR system is deduced.In the second level,the waveform design and imaging method of the low-intercept RF SAR system are given and verified by simulation.Finally,the main technical characteristics of the lowintercept RF stealth SAR system are given to guide the design of low-intercept RF stealth SAR system.展开更多
To automatically detect oil tanks in polarimetric synthetic aperture radar(SAR) images, a coastal oil tank detection method is proposed based on recognition of T-shaped harbor. First of all, the T-shaped harbor is d...To automatically detect oil tanks in polarimetric synthetic aperture radar(SAR) images, a coastal oil tank detection method is proposed based on recognition of T-shaped harbor. First of all, the T-shaped harbor is detected to locate the region of interest(ROI) of oil tanks. Then all suspicious targets in the ROI are extracted by the segmentation of strong scattering targets and the classifier of H/α. The template targets are selected from the suspicious targets by the combination of a proposed circular degree parameter and the similarity parameter(SP) of the polarimetric coherency matrix. Finally, oil tanks are detected according to the statistics of the similarity parameter between each suspicious target and template targets in ROI. Polarimetric SAR data acquired by RADARSAT-2 over Berkeley and Singapore areas are used for testing. Experiment results show that most of the targets are correctly detected and the overall detection rate is close to 80%.The false rate is effectively reduced by the proposed algorithm compared with the method without T-shaped harbor recognition.展开更多
SAR images commonly suffer fromspeckle noise,posing a significant challenge in their analysis and interpretation.Existing convolutional neural network(CNN)based despeckling methods have shown great performance in remo...SAR images commonly suffer fromspeckle noise,posing a significant challenge in their analysis and interpretation.Existing convolutional neural network(CNN)based despeckling methods have shown great performance in removing speckle noise.However,these CNN-basedmethods have a fewlimitations.They do not decouple complex background information in amulti-resolutionmanner.Moreover,they have deep network structures thatmay result in many parameters,limiting their applicability tomobile devices.Furthermore,extracting key speckle information in the presence of complex background is also a major problem with SAR.The proposed study addresses these limitations by introducing a lightweight pyramid and attention-based despeckling(PAN-Despeck)network.The primary objective is to enhance image quality and enable improved information interpretation,particularly on mobile devices and scenarios involving complex backgrounds.The PAN-Despeck network leverages domainspecific knowledge and integrates Gaussian Laplacian image pyramid decomposition for multi-resolution image analysis.By utilizing this approach,complex background information can be effectively decoupled,leading to enhanced despeckling performance.Furthermore,the attention mechanism selectively focuses on key speckle features and facilitates complex background removal.The network incorporates recursive and residual blocks to ensure computational efficiency and accelerate training speed,making it lightweight while maintaining high performance.Through comprehensive evaluations,it is demonstrated that PAN-Despeck outperforms existing image restoration methods.With an impressive average peak signal-to-noise ratio(PSNR)of 28.355114 and a remarkable structural similarity index(SSIM)of 0.905467,it demonstrates exceptional performance in effectively reducing speckle noise in SAR images.The source code for the PAN-DeSpeck network is available on GitHub.展开更多
The conventional two dimensional(2D)inverse synthetic aperture radar(ISAR)imaging fails to provide the targets'three dimensional(3D)information.In this paper,a 3D ISAR imaging method for the space target is propos...The conventional two dimensional(2D)inverse synthetic aperture radar(ISAR)imaging fails to provide the targets'three dimensional(3D)information.In this paper,a 3D ISAR imaging method for the space target is proposed based on mutliorbit observation data and an improved orthogonal matching pursuit(OMP)algorithm.Firstly,the 3D scattered field data is converted into a set of 2D matrix by stacking slices of the 3D data along the elevation direction dimension.Then,an improved OMP algorithm is applied to recover the space target's amplitude information via the 2D matrix data.Finally,scattering centers can be reconstructed with specific three dimensional locations.Numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed 3D imaging method.展开更多
A new constant false alarm rate (CFAR) target detector for synthetic aperture radar (SAR) images is developed. For each pixel under test, both the local probability density function (PDF) of the pixel and the cl...A new constant false alarm rate (CFAR) target detector for synthetic aperture radar (SAR) images is developed. For each pixel under test, both the local probability density function (PDF) of the pixel and the clutter PDF in the reference window are estimated by the non-parametric density estimation. The target detector is defined as the mean square error (MSE) distance between the two PDFs. The CFAR detection in SAR images having multiplicative noise is achieved by adaptive kernel bandwidth proportional to the clutter level. In addition, for obtaining a threshold with respect to a given probability of false alarm (PFA), an unsupervised null distribution fitting method with outlier rejection is proposed. The effectiveness of the proposed target detector is demonstrated by the experiment result using the RADATSAT-2 SAR image.展开更多
A method and procedure is presented to reconstruct three-dimensional(3D) positions of scattering centers from multiple synthetic aperture radar(SAR) images. Firstly, two-dimensional(2D) attribute scattering centers of...A method and procedure is presented to reconstruct three-dimensional(3D) positions of scattering centers from multiple synthetic aperture radar(SAR) images. Firstly, two-dimensional(2D) attribute scattering centers of targets are extracted from 2D SAR images. Secondly, similarity measure is developed based on 2D attributed scatter centers' location, type, and radargrammetry principle between multiple SAR images. By this similarity, we can associate 2D scatter centers and then obtain candidate 3D scattering centers. Thirdly, these candidate scattering centers are clustered in 3D space to reconstruct final 3D positions. Compared with presented methods, the proposed method has a capability of describing distributed scattering center, reduces false and missing 3D scattering centers, and has fewer restrictionson modeling data. Finally, results of experiments have demonstrated the effectiveness of the proposed method.展开更多
Based on dual-frequencies dual-apertures spaceborne SAR (Synthetic Aperture Radar), a new SAR system with four receiving channels and two operation modes is presented in this paper. SAR imaging and Moving Target Indic...Based on dual-frequencies dual-apertures spaceborne SAR (Synthetic Aperture Radar), a new SAR system with four receiving channels and two operation modes is presented in this paper. SAR imaging and Moving Target Indication (MTI) are studied in this system. High resolution imaging with wide swath is implemented by the Mode I, and MTI is completed by the Mode II. High azimuth resolution is achieved by the Displaced Phase Center (DPC) multibeam technique. And the Coherent Accumulation (CA) method, which combines dual channels data of different carrier frequency, is used to enhance the range resolution. For the data of different carrier frequency, the two aperture inter- ferometric processing is executed to implement clutter cancellation, respectively. And the couple of clutter suppressed data are employed to implement Dual Carrier Frequency Conjugate Processing (DCFCP), then both slow and fast moving targets detection can be completed, followed by moving target imaging. The simulation results show the validity of the signal processing method of this new SAR system.展开更多
Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar (ISAR) target recognition, this paper imports the concept of visual perception and presents a novel method, which is ba...Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar (ISAR) target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding (NNSC) and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis (PCA) and non-negative matrix factorization (NMF) via simulations.展开更多
Improving the target-clutter ratio (TCR) of moving targets in synthetic aperture radar (SAR), imagery is very important for target detection and identification. In this paper, using the Cloude's decomposition the...Improving the target-clutter ratio (TCR) of moving targets in synthetic aperture radar (SAR), imagery is very important for target detection and identification. In this paper, using the Cloude's decomposition theory, an average eovarianee matrix can be decomposed into a summation of matrices representing three different scattering processes: the single bounce scattering, double bounce scattering, and diffuse scattering. A new idea of using the combination of the three components to enhance the contrast of an image is proposed. In order to compare with the polarimetric contrast enhancement method based on HH, HV, and W data, ship areas of two combinatorial intensity images are detected by image binarization. Experimental results show that the method proposed in this paper provides better contrast.展开更多
The airborne cross-track three apertures MilliMeter Wave (MMW) Synthetic Aperture Radar (SAR) side-looking three-Dimensional (3D) imaging is investigated in this paper. Three apertures are distributed along the cross-...The airborne cross-track three apertures MilliMeter Wave (MMW) Synthetic Aperture Radar (SAR) side-looking three-Dimensional (3D) imaging is investigated in this paper. Three apertures are distributed along the cross-track direction, and three virtual phase centers will be obtained through one-input and three-output. These three virtual phase centers form a sparse array which can be used to obtain the cross-track resolution. Because the cross-track array is short, the cross-track resolution is low. When the system works in side-looking mode, the cross-track resolution and height resolution will be coupling, and the low cross-track resolution will partly be transformed into the height uncertainty. The beam pattern of the real aperture is used as a weight to improve the Peak to SideLobe Ratio (PSLR) and Integrated SideLobe Ratio (ISLR) of the cross-track sparse array. In order to suppress the high cross-track sidelobes, a weighting preprocessing method is proposed. The 3D images of a point target and a simulation scene are achieved to verify the feasibility of the proposed method. And the imaging result of the real data obtained by the cross-track three-baseline MMW InSAR prototype is presented as a beneficial attempt.展开更多
The paper presents an algorithm of automatic target detection in Synthetic Aperture Radar(SAR) images based on Maximum A Posteriori(MAP). The algorithm is divided into three steps. First, it employs Gaussian mixture d...The paper presents an algorithm of automatic target detection in Synthetic Aperture Radar(SAR) images based on Maximum A Posteriori(MAP). The algorithm is divided into three steps. First, it employs Gaussian mixture distribution to approximate and estimate multi-modal histogram of SAR image. Then, based on the principle of MAP, when a priori probability is both unknown and learned respectively, the sample pixels are classified into different classes c = {target,shadow, background}. Last, it compares the results of two different target detections. Simulation results preferably indicate that the presented algorithm is fast and robust, with the learned a priori probability, an approach to target detection is reliable and promising.展开更多
In recent years,deep learning algorithms have been popular in recognizing targets in synthetic aperture radar(SAR)images.However,due to the problem of overfitting,the performance of these models tends to worsen when j...In recent years,deep learning algorithms have been popular in recognizing targets in synthetic aperture radar(SAR)images.However,due to the problem of overfitting,the performance of these models tends to worsen when just a small number of training data are available.In order to solve the problems of overfitting and an unsatisfied performance of the network model in the small sample remote sensing image target recognition,in this paper,we uses a deep residual network to autonomously acquire image features and proposes the Deep Feature Bayesian Classifier model(RBnet)for SAR image target recognition.In the RBnet,a Bayesian classifier is used to improve the effect of SAR image target recognition and improve the accuracy when the training data is limited.The experimental results on MSTAR dataset show that the RBnet can fully exploit effective information in limited samples and recognize the target of the SAR images more accurately.Compared with other state-of-the-art methods,our method offers significant recognition accuracy improvements under limited training data.Noted that theRBnet is moderately difficult to implement and has the value of popularization and application in engineering application scenarios in the field of small-sample remote sensing target recognition and recognition.展开更多
Synthetic aperture radar(SAR) automatic target recognition is an important application in SAR.How to extract features has restricted the application of SAR technology seriously.In this paper,a new feature extraction m...Synthetic aperture radar(SAR) automatic target recognition is an important application in SAR.How to extract features has restricted the application of SAR technology seriously.In this paper,a new feature extraction method for SAR automatic target recognition based on maximum interclass distance is proposed,which integrates class and neighborhood information.This method can reinforce discriminative power using maximum interclass distance,so it can improve recognition rate effectively.展开更多
文摘The method of moving target detection based on subimage cancellation for single-antenna airborne SAR is presented. First the subimage is obtained through frequency processing is pointed out. The imaging difference of a stationary objects and moving object in the subimage based on the frequency division is analyzed from the fundamental principle. Then the developed method combines the shear averaging algorithm to focus on the moving target in the subimage, after the clutter suppression and the focusing position in each subimage is obtained. Next the observation model and the relative movement of the moving targets between the subimages estimate the moving targets. The theoretical analysis and simulation results demonstrate that the method is effective and can not only detect the moving targets, but also estimate their motion parameters precisely.
基金This work was supported by the National Science Fund for Distinguished Young Scholars(62325104).
文摘The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.
基金Project(61360020102) supported by the National Basic Research Development Program of China
文摘For ballistic mid-course targets,in addition to constant orbital motion,the target or any structure on the target undergoes micro-motion dynamics,such as spin,precession and tumbling.The micro-motion characteristics of the ballistic mid-course targets were discussed.The target motion model and inverse synthetic aperture radar(ISAR) imaging model for this kind of targets were built.Then,the influence of micro-motion on ISAR imaging based on the established imaging model was presented.The computer simulation to get mid-course target echoes from static darkroom electromagnetic scattering data based on the established target motion model was realized.The imaging results of computer simulation show the validity of ISAR imaging analysis for micro-motion targets.
基金supported by the National Natural Science Foundation of China(6107113961471019+5 种基金61171122)the Aeronautical Science Foundation of China(20142051022)the Foundation of ATR Key Lab(C80264)the National Natural Science Foundation of China(NNSFC)under the RSE-NNSFC Joint Project(2012-2014)(61211130210)with Beihang Universitythe RSE-NNSFC Joint Project(2012-2014)(61211130309)with Anhui Universitythe"Sino-UK Higher Education Research Partnership for Ph D Studies"Joint Project(2013-2015)
文摘Current research on target detection and recognition from synthetic aperture radar (SAR) images is usually carried out separately. It is difficult to verify the ability of a target recognition algorithm for adapting to changes in the environment. To realize the whole process of SAR automatic target recognition (ATR), es- pecially for the detection and recognition of vehicles, an algorithm based on kernel fisher discdminant analysis (KFDA) is proposed. First, in order to make a better description of the difference be- tween the background and the target, KFDA is extended to the detection part. Image samples are obtained with a dual-window approach and features of the inner and outer window samples are extracted by using KFDA. The difference between the features of inner and outer window samples is compared with a threshold to determine whether a vehicle exists. Second, for the target area, we propose an improved KFDA-IMED (image Euclidean distance) combined with a support vector machine (SVM) to recognize the vehicles. Experimental results validate the performance of our method. On the detection task, our proposed method obtains not only a high detection rate but also a low false alarm rate without using any prior information. For the recognition task, our method overcomes the SAR image aspect angle sensitivity, reduces the requirements for image preprocessing and improves the recogni- tion rate.
基金Project(61105020)supported by the National Natural Science Foundation of ChinaProject(13zxtk08)supported by the Key Research Platform for Research Projects of Southwest University of Science and Technology,China
文摘The accuracy of background clutter model is a key factor which determines the performance of a constant false alarm rate(CFAR) target detection method. G0 distribution is one of the optimal statistic models in the synthetic aperture radar(SAR) image background clutter modeling and can accurately model various complex background clutters in the SAR images. But the application of the distribution is greatly limited by its disadvantages that the parameter estimation is complex and the local detection threshold is difficult to be obtained. In order to solve the above-mentioned problems, an synthetic aperture radar CFAR target detection method using the logarithmic cumulant(Mo LC) + method of moment(Mo M)-based G0 distribution clutter model is proposed. In the method, G0 distribution is used for modeling the background clutters, a new Mo LC+Mo M-based parameter estimation method coupled with a fast iterative algorithm is used for estimating the parameters of G0 distribution and an exquisite dichotomy method is used for obtaining the local detection threshold of CFAR detection, which greatly improves the computational efficiency, detection performance and environmental adaptability of CFAR detection. Experimental results show that the proposed SAR CFAR target detection method has good target detection performance in various complex background clutter environments.
基金supported by the National Key R&D Program of China(2017YFC1405600)the Fundamental Research Funds for the Central Universities(JB180213)
文摘Not confined to a certain point,such as waveform,this paper systematically studies the low-intercept radio frequency(RF)stealth design of synthetic aperture radar(SAR)from the system level.The study is carried out from two levels.In the first level,the maximum low-intercept range equation of the conventional SAR system is deduced firstly,and then the maximum low-intercept range equation of the multiple-input multiple-output SAR system is deduced.In the second level,the waveform design and imaging method of the low-intercept RF SAR system are given and verified by simulation.Finally,the main technical characteristics of the lowintercept RF stealth SAR system are given to guide the design of low-intercept RF stealth SAR system.
基金supported by the National Key R&D Program of China(2017YFB0502700)the National Natural Science Foundation of China(61490693+3 种基金61771043)the High-Resolution Earth Observation Systems(41-Y20A14-9001-15/1630-Y20A12-9004-15/1630-Y20A10-9001-15/16)
文摘To automatically detect oil tanks in polarimetric synthetic aperture radar(SAR) images, a coastal oil tank detection method is proposed based on recognition of T-shaped harbor. First of all, the T-shaped harbor is detected to locate the region of interest(ROI) of oil tanks. Then all suspicious targets in the ROI are extracted by the segmentation of strong scattering targets and the classifier of H/α. The template targets are selected from the suspicious targets by the combination of a proposed circular degree parameter and the similarity parameter(SP) of the polarimetric coherency matrix. Finally, oil tanks are detected according to the statistics of the similarity parameter between each suspicious target and template targets in ROI. Polarimetric SAR data acquired by RADARSAT-2 over Berkeley and Singapore areas are used for testing. Experiment results show that most of the targets are correctly detected and the overall detection rate is close to 80%.The false rate is effectively reduced by the proposed algorithm compared with the method without T-shaped harbor recognition.
文摘SAR images commonly suffer fromspeckle noise,posing a significant challenge in their analysis and interpretation.Existing convolutional neural network(CNN)based despeckling methods have shown great performance in removing speckle noise.However,these CNN-basedmethods have a fewlimitations.They do not decouple complex background information in amulti-resolutionmanner.Moreover,they have deep network structures thatmay result in many parameters,limiting their applicability tomobile devices.Furthermore,extracting key speckle information in the presence of complex background is also a major problem with SAR.The proposed study addresses these limitations by introducing a lightweight pyramid and attention-based despeckling(PAN-Despeck)network.The primary objective is to enhance image quality and enable improved information interpretation,particularly on mobile devices and scenarios involving complex backgrounds.The PAN-Despeck network leverages domainspecific knowledge and integrates Gaussian Laplacian image pyramid decomposition for multi-resolution image analysis.By utilizing this approach,complex background information can be effectively decoupled,leading to enhanced despeckling performance.Furthermore,the attention mechanism selectively focuses on key speckle features and facilitates complex background removal.The network incorporates recursive and residual blocks to ensure computational efficiency and accelerate training speed,making it lightweight while maintaining high performance.Through comprehensive evaluations,it is demonstrated that PAN-Despeck outperforms existing image restoration methods.With an impressive average peak signal-to-noise ratio(PSNR)of 28.355114 and a remarkable structural similarity index(SSIM)of 0.905467,it demonstrates exceptional performance in effectively reducing speckle noise in SAR images.The source code for the PAN-DeSpeck network is available on GitHub.
文摘The conventional two dimensional(2D)inverse synthetic aperture radar(ISAR)imaging fails to provide the targets'three dimensional(3D)information.In this paper,a 3D ISAR imaging method for the space target is proposed based on mutliorbit observation data and an improved orthogonal matching pursuit(OMP)algorithm.Firstly,the 3D scattered field data is converted into a set of 2D matrix by stacking slices of the 3D data along the elevation direction dimension.Then,an improved OMP algorithm is applied to recover the space target's amplitude information via the 2D matrix data.Finally,scattering centers can be reconstructed with specific three dimensional locations.Numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed 3D imaging method.
基金supported by the National Natural Science Foundation of China (40871157 41171317)the Foundation of Advance Research of Science and Technology for Chinese National Defence(9140C620201902)
文摘A new constant false alarm rate (CFAR) target detector for synthetic aperture radar (SAR) images is developed. For each pixel under test, both the local probability density function (PDF) of the pixel and the clutter PDF in the reference window are estimated by the non-parametric density estimation. The target detector is defined as the mean square error (MSE) distance between the two PDFs. The CFAR detection in SAR images having multiplicative noise is achieved by adaptive kernel bandwidth proportional to the clutter level. In addition, for obtaining a threshold with respect to a given probability of false alarm (PFA), an unsupervised null distribution fitting method with outlier rejection is proposed. The effectiveness of the proposed target detector is demonstrated by the experiment result using the RADATSAT-2 SAR image.
文摘A method and procedure is presented to reconstruct three-dimensional(3D) positions of scattering centers from multiple synthetic aperture radar(SAR) images. Firstly, two-dimensional(2D) attribute scattering centers of targets are extracted from 2D SAR images. Secondly, similarity measure is developed based on 2D attributed scatter centers' location, type, and radargrammetry principle between multiple SAR images. By this similarity, we can associate 2D scatter centers and then obtain candidate 3D scattering centers. Thirdly, these candidate scattering centers are clustered in 3D space to reconstruct final 3D positions. Compared with presented methods, the proposed method has a capability of describing distributed scattering center, reduces false and missing 3D scattering centers, and has fewer restrictionson modeling data. Finally, results of experiments have demonstrated the effectiveness of the proposed method.
基金Supported by the National Natural Science Foundation of China (NSFC) (No.60772103)China National Key Laboratory of Microwave Imaging Technology Foundation (No.9140C1903050804)
文摘Based on dual-frequencies dual-apertures spaceborne SAR (Synthetic Aperture Radar), a new SAR system with four receiving channels and two operation modes is presented in this paper. SAR imaging and Moving Target Indication (MTI) are studied in this system. High resolution imaging with wide swath is implemented by the Mode I, and MTI is completed by the Mode II. High azimuth resolution is achieved by the Displaced Phase Center (DPC) multibeam technique. And the Coherent Accumulation (CA) method, which combines dual channels data of different carrier frequency, is used to enhance the range resolution. For the data of different carrier frequency, the two aperture inter- ferometric processing is executed to implement clutter cancellation, respectively. And the couple of clutter suppressed data are employed to implement Dual Carrier Frequency Conjugate Processing (DCFCP), then both slow and fast moving targets detection can be completed, followed by moving target imaging. The simulation results show the validity of the signal processing method of this new SAR system.
基金supported by the Prominent Youth Fund of the National Natural Science Foundation of China (61025006)
文摘Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar (ISAR) target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding (NNSC) and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis (PCA) and non-negative matrix factorization (NMF) via simulations.
基金supported in part by the 863 Program under Grant No. 2007AA12Z159the Program for New Century Excellent Talents in University of China under Grant No. NCET-06-0813
文摘Improving the target-clutter ratio (TCR) of moving targets in synthetic aperture radar (SAR), imagery is very important for target detection and identification. In this paper, using the Cloude's decomposition theory, an average eovarianee matrix can be decomposed into a summation of matrices representing three different scattering processes: the single bounce scattering, double bounce scattering, and diffuse scattering. A new idea of using the combination of the three components to enhance the contrast of an image is proposed. In order to compare with the polarimetric contrast enhancement method based on HH, HV, and W data, ship areas of two combinatorial intensity images are detected by image binarization. Experimental results show that the method proposed in this paper provides better contrast.
基金Supported by the National Basic Research Program (973) of China (No. 2009CB72400)
文摘The airborne cross-track three apertures MilliMeter Wave (MMW) Synthetic Aperture Radar (SAR) side-looking three-Dimensional (3D) imaging is investigated in this paper. Three apertures are distributed along the cross-track direction, and three virtual phase centers will be obtained through one-input and three-output. These three virtual phase centers form a sparse array which can be used to obtain the cross-track resolution. Because the cross-track array is short, the cross-track resolution is low. When the system works in side-looking mode, the cross-track resolution and height resolution will be coupling, and the low cross-track resolution will partly be transformed into the height uncertainty. The beam pattern of the real aperture is used as a weight to improve the Peak to SideLobe Ratio (PSLR) and Integrated SideLobe Ratio (ISLR) of the cross-track sparse array. In order to suppress the high cross-track sidelobes, a weighting preprocessing method is proposed. The 3D images of a point target and a simulation scene are achieved to verify the feasibility of the proposed method. And the imaging result of the real data obtained by the cross-track three-baseline MMW InSAR prototype is presented as a beneficial attempt.
文摘The paper presents an algorithm of automatic target detection in Synthetic Aperture Radar(SAR) images based on Maximum A Posteriori(MAP). The algorithm is divided into three steps. First, it employs Gaussian mixture distribution to approximate and estimate multi-modal histogram of SAR image. Then, based on the principle of MAP, when a priori probability is both unknown and learned respectively, the sample pixels are classified into different classes c = {target,shadow, background}. Last, it compares the results of two different target detections. Simulation results preferably indicate that the presented algorithm is fast and robust, with the learned a priori probability, an approach to target detection is reliable and promising.
基金funded by the National Key R&D Program of China(2021YFC3320302).
文摘In recent years,deep learning algorithms have been popular in recognizing targets in synthetic aperture radar(SAR)images.However,due to the problem of overfitting,the performance of these models tends to worsen when just a small number of training data are available.In order to solve the problems of overfitting and an unsatisfied performance of the network model in the small sample remote sensing image target recognition,in this paper,we uses a deep residual network to autonomously acquire image features and proposes the Deep Feature Bayesian Classifier model(RBnet)for SAR image target recognition.In the RBnet,a Bayesian classifier is used to improve the effect of SAR image target recognition and improve the accuracy when the training data is limited.The experimental results on MSTAR dataset show that the RBnet can fully exploit effective information in limited samples and recognize the target of the SAR images more accurately.Compared with other state-of-the-art methods,our method offers significant recognition accuracy improvements under limited training data.Noted that theRBnet is moderately difficult to implement and has the value of popularization and application in engineering application scenarios in the field of small-sample remote sensing target recognition and recognition.
基金supported in part by the National High-tech Research and Development Program("863"Program)of China(Grant No.2009AA12Z106)
文摘Synthetic aperture radar(SAR) automatic target recognition is an important application in SAR.How to extract features has restricted the application of SAR technology seriously.In this paper,a new feature extraction method for SAR automatic target recognition based on maximum interclass distance is proposed,which integrates class and neighborhood information.This method can reinforce discriminative power using maximum interclass distance,so it can improve recognition rate effectively.