Multi-view Synthetic Aperture Radar Research Articles

A Robust Registration Method for Multi-view SAR Images based on Best Buddy Similarity

Abstract. Due to the influence of imaging angle and terrain undulation, multi-view synthetic aperture radar (SAR) images are difficult to be directly registered by traditional methods. Although feature matching solves the issue of image rotation and maintains scale invariance, these methods often lead to non-uniformity of interest points and may not achieve subpixel accuracy. The traditional template matching method makes it difficult to generate correct matches for multi-view SAR oblique images. In this paper, a multi-view SAR image template matching method based on Best Buddy Similarity (BBS) is proposed to solve the traditional methods' problem. Firstly, the initial correspondences between images are established according to the Range-Doppler model of SAR images. Secondly, a sliding window search is performed on the established correspondence, the BBS is calculated, and the subpixel locations of the peaks on the similarity map are estimated to achieve a fine match. In the calculation process of BBS, the SAR-ROEWA operator is used to suppress the speckle noise of SAR images. The experiment demonstrated that SAR-BBS can accurately match SAR images with large rotation angle. The peak value on the search window is significant. The registration accuracy of SAR-BBS outperforms the other state-of-the-art methods.

Recognition for SAR deformation military target from a new MiniSAR dataset using multi-view joint transformer approach

Accurately detecting ground armored weapons is crucial for achieving initiative advantages in military operations. Generally, satellite or airborne synthetic aperture radar (SAR) systems face limitations due to their revisit cycles and fixed flight trajectories, resulting in single-view imaging of targets, thereby hampering the recognition of small SAR ground targets. In contrast, MiniSAR possesses the capability to capture the multi-view of a target by acquiring images from different azimuth angles. In this research, our team utilizes a self-developed MiniSAR system to generate multi-view SAR images of real ground armored targets and recognize targets. However, the recognition of small targets in SAR images encounters two significant difficulties. First, small targets in SAR images are prone to interference from background noise. Second, SAR target deformation arises from variations in depression angles and imaging processes. To tackle these difficulties, this paper proposes a novel SAR ground deformation target recognition approach based on a joint multi-view transformer model. The method first preprocesses SAR images based on a low-frequency priori SAR image denoising method. Next, it obtains multi-view joint information through a self-attentive mechanism, inputs joint features to the transformer structure. The outputs are jointly updated by a multi-way averaging adaptive loss function to improve the recognition accuracy of deformed targets. The experimental results demonstrate the superiority of the proposed method in SAR ground deformation target recognition, outperforming other representative approaches such as information fusion of target and shadow (IFTS) and Vision Transformer (ViT). It is concluded that the proposed method has high recognition accuracies of 98.37% and 93.86 % on the moving and stationary target acquisition and recognition (Mstar) and our SAR images dataset, respectively, in the field of SAR ground deformation target recognition. We have included links to the code and data in the abstract of this paper for ease of access. The source code and sample dataset are available at https://github.com/Lvjiming/MJT.

Multiview synthetic aperture radar target recognition method using joint sparse representation and random weight matrix

Multiview synthetic aperture radar (SAR) images could provide richer information for target recognition as reported in previous works. This study exploits multiview SAR images with application to target recognition via the combination of joint sparse representation (JSR) and random weight matrix. First, the multiview SAR images are clustered into several view sets via a coarse clustering algorithm. In this way, the views in each set tend to share high correlations so they can be properly represented with JSR. For the reconstruction errors from different views, they are fused through a random weight matrix, which includes a rich set of linear weight vectors. Afterwards, the fused errors at different choices of weight vectors are analyzed to form a decision value for target recognition, which could better reflect the differences between different classes through multiview SAR images in comparison with traditional JSR-based methods. Therefore, the overall SAR target recognition performance can be effectively enhanced. Experiments are investigated on the moving and stationary target acquisition and recognition dataset under several operating conditions. The results reveal the high effectiveness of the proposed method under standard operating condition and good robustness under typical extended operating conditions including depression angle variance, configuration variants, noise corruption, partial occlusion, and resolution variance.

A Probabilistic Approach for Stereo 3D Point Cloud Reconstruction from Airborne Single-Channel Multi-Aspect SAR Image Sequences

We investigate the problem of obtaining dense 3D reconstruction from airborne multi-aspect synthetic aperture radar (SAR) image sequences. Dense 3D reconstructions of multi-view SAR images are vulnerable to anisotropic scatters. To address this issue, we propose a probabilistic 3D reconstruction method based on jointly estimating the pixel’s height and degree of anisotropy. Specifically, we propose a mixture distribution model for the stereo-matching results, where the degree of anisotropy is modeled as an underlying error source. Then, a Bayesian filtering method is proposed for dense 3D point cloud generation. For real-time applications, redundancy in multi-aspect observations is further exploited in a probabilistic manner to accelerate the stereo-reconstruction process. To verify the effectiveness and reliability of the proposed method, 3D point cloud generation is tested on Ku-band drone SAR data for a domestic airport area.

A multi-view SAR target recognition method using feature fusion and joint classification

ABSTRACT To handle synthetic aperture radar (SAR) image target recognition problem, a multi-view method is proposed. For the multi-view SAR images be recognized, they are first clustered based on the correlation coefficients and divided into several view sets. Afterwards, for the view set containing two or more images, the multiset canonical correlation analysis (MCCA) is employed to fuse them as a single feature vector. For the view set with a single image, its corresponding feature vector is directly used. Finally, the joint sparse representation is used to characterize and classify the feature vectors from different view sets and determine the target label of the multi-view SAR images. Experiments and analysis on the moving and stationary target acquisition and recognition (MSTAR) dataset show that the proposed method can achieve an average recognition rate of 99.42% for 10 types of targets under the standard operating condition (SOC). Its performance is also better than several reference methods under the extended operating conditions (EOC) including noise interference and target occlusion.

FEF-Net: A Deep Learning Approach to Multiview SAR Image Target Recognition

Synthetic aperture radar (SAR) is an advanced microwave imaging system of great importance. The recognition of real-world targets from SAR images, i.e., automatic target recognition (ATR), is an attractive but challenging issue. The majority of existing SAR ATR methods are designed for single-view SAR images. However, multiview SAR images contain more abundant classification information than single-view SAR images, which benefits automatic target classification and recognition. This paper proposes an end-to-end deep feature extraction and fusion network (FEF-Net) that can effectively exploit recognition information from multiview SAR images and can boost the target recognition performance. The proposed FEF-Net is based on a multiple-input network structure with some distinct and useful learning modules, such as deformable convolution and squeeze-and-excitation (SE). Multiview recognition information can be effectively extracted and fused with these modules. Therefore, excellent multiview SAR target recognition performance can be achieved by the proposed FEF-Net. The superiority of the proposed FEF-Net was validated based on experiments with the moving and stationary target acquisition and recognition (MSTAR) dataset.

Multiview Deep Feature Learning Network for SAR Automatic Target Recognition

Multiview synthetic aperture radar (SAR) images contain much richer information for automatic target recognition (ATR) than a single-view one. It is desirable to establish a reasonable multiview ATR scheme and design effective ATR algorithm to thoroughly learn and extract that classification information, so that superior SAR ATR performance can be achieved. Hence, a general processing framework applicable for a multiview SAR ATR pattern is first given in this paper, which can provide an effective approach to ATR system design. Then, a new ATR method using a multiview deep feature learning network is designed based on the proposed multiview ATR framework. The proposed neural network is with a multiple input parallel topology and some distinct deep feature learning modules, with which significant classification features, the intra-view and inter-view features existing in the input multiview SAR images, will be learned simultaneously and thoroughly. Therefore, the proposed multiview deep feature learning network can achieve an excellent SAR ATR performance. Experimental results have shown the superiorities of the proposed multiview SAR ATR method under various operating conditions.

Discrimination and Correlation Analysis of Multiview SAR Images with Application to Target Recognition

A multiview synthetic aperture radar (SAR) target recognition with discrimination and correlation analysis is proposed in this study. The multiple views are first prescreened by a support vector machine (SVM) to select out those highly discriminative ones. These views are then clustered into several view sets, in which images share high correlations. The joint sparse representation (JSR) is adopted to classify SAR images in each view set, and all the decisions from different view sets are fused using a linear weighting strategy. The proposed method makes more sufficient analysis of the multiview SAR images so the recognition performance can be effectively enhanced. To test the proposed method, experiments are set up based on the moving and stationary target acquisition and recognition (MSTAR) dataset. The results show that the proposed method could achieve superior performance under different situations over some compared methods.

Fast Registration of Multiview Slant-Range SAR Images

Unlike geocoded images, slant-range (SR) synthetic aperture radar (SAR) images vary from imaging resolution to angles, which are difficult to be registered directly using the traditional SAR image registration methods. A possible way is to match their corresponding geocoded images and to project the correspondences to SR images. However, this way is time consuming and suffers from both registration and projection errors. In this letter, an automatic and efficient method is proposed to directly match multiview SR SAR images. We first estimate the scale and rotation differences between two SR images from the metadata delivered by vendors alongside the image file. Specifically, the scale differences of the range and azimuth directions are estimated by transforming the range and azimuth pixel intervals into a uniform geographical resolution, and the rotation differences are estimated by comparing the azimuth angles of an image-pair. A global-to-local framework is then implemented to accelerate the registration process. In the global stage, we fix the scale and rotation parameters in SAR-scale-invariant-feature-transform (SAR-SIFT) method to avoid mismatches. In the local stage, the phase correlation of cropped patches is parallelized to generate accurate matches. Experimental results on 13 multiview SAR images of the Omaha city show that the proposed method can provide accurate and efficient registration results for each pair of the 13 images, and outperforms the state-of-the-art methods both in accuracy and in efficiency.

A Multiview SAR Target Recognition Method Using Inner Correlation Analysis.

This paper proposes a synthetic aperture radar (SAR) image target recognition method using multiple views and inner correlation analysis. Due to the azimuth sensitivity of SAR images, the inner correlation between multiview images participating in recognition is not stable enough. To this end, the proposed method first clusters multiview SAR images based on image correlation and nonlinear correlation information entropy (NCIE) in order to obtain multiple view sets with strong internal correlations. For each view set, the multitask sparse representation is used to reconstruct the SAR images in it to obtain high-precision reconstructions. Finally, the linear weighting method is used to fuse the reconstruction errors from different view sets and the target category is determined according to the fusion error. In the experiment, the tests are conducted based on the MSTAR dataset, and the results validate the effectiveness of the proposed method.

Selection of multi-view SAR images via nonlinear correlation information entropy with application to target classification

ABSTRACT A synthetic aperture radar (SAR) target classification method is proposed by properly selecting multiple views via the nonlinear correlation information entropy (NCIE). The optimal subset of multi-view SAR images are selected, which are assumed to share stable inner correlations. The joint sparse representation is adopted as the basic classification scheme for the selected views to exploit their inner correlations. According to the total reconstruction error of the selected multi-view SAR images, the target label can be decided. The Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is employed to test the proposed method and the results validate the its superiority over some reference methods.

Azimuth Cutoff Compensation Method for SAR Wave Observation Based on Multiview Wave Spectrum Data Fusion

Azimuth cutoff is an inherent disadvantage of synthetic aperture radar (SAR) waves observation. The waves shorter than some certain wavelength (azimuth cutoff) cannot be imaged in original form (structures) by SAR. For a single SAR observation, the problem of the azimuth cutoff for ocean waves can be resolved to some extent by cooperative observations of SAR satellites. Multiple SAR satellites are required to achieve simultaneous observation of an ocean area to obtain multiview SAR ocean wave synchronization data. Currently, these data cannot be acquired from in-orbit SARs. In this study, imaging simulations of multiview SAR ocean wave synchronization data based on small SAR satellites were carried out for the first time with X-band, 4-m resolution, stripmap mode and Single Look Complex (SLC) product. The Max Planck Institute (MPI) method was used to obtain the optimum wave spectrum of the synchronous data. The influencing factors of the azimuth cutoff wavelength were analyzed by using measured and simulated SAR data. The analysis results were used to develop a novel multiview wave spectrum data fusion method for azimuth cutoff compensation. The azimuth cutoff compensation effect was evaluated by comparing the inversion results before and after data fusion: the azimuth cutoff decreased by 9.76% on average, the root mean square error (RMSE) of the significant wave height is 0.06m, and RMSE of the mean wave period is 0.58s. The azimuth cutoff compensation method can be applied to SAR data for medium and low sea states (that is, wind speeds of 5-15 m/s). These results show that the proposed method of multiview wave spectrum data fusion effectively compensates for azimuth cutoff.

3D reconstruction and error analysis of multi‐view space‐borne SAR images under different configurations

Synthetic aperture radar (SAR) has a good ability to detect the microwave scattering characteristics of the target and has a good capability of slant range Doppler positioning. Using multi-view SAR images in combination with image matching and positioning equations, the 3D position of the target can be obtained. In the past, multi-view 3D reconstruction mainly used side-looking images with parallel trajectories but different incident angles. That method is not universal for different configurations and lacks analysis of the relationship between solution and parameter error. This study aims at the problem of multi-view SAR 3D reconstruction. The authors establish general 3D reconstruction equations that can be used for non-parallel track and non-side-looking and the solution method is deduced. Based on this, an analysis method of error sensitivity is proposed and the dependence relationship between the reconstruction error and configuration, velocity, position, and slant range is analysed. The correctness of this method is verified by simulation experiments, which provides guidance for selection of configuration and sensor accuracy when applying 3D reconstruction.

SAR target recognition via sparse representation of multi-view SAR images with correlation analysis

This study proposes a synthetic aperture radar (SAR) target recognition method via sparse representation of multi-view images with correlation analysis. The multi-view SAR images are first clustered into several view sets and in each set the included SAR images share high correlations. For the view set with only one SAR image, the sparse representation-based classification (SRC) is used for classification. The joint sparse representation (JSR) is employed to classify the view sets with more than one images in order to exploit their correlations. The decisions from different view sets are then fused based on the Bayesian theory. Therefore, both the independency and inner correlations in the multi-view SAR images can be better exploited to improve the target recognition performance. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) data set. The results show the superiority of the proposed approach over some other methods.

Multi-View Tensor Sparse Representation Model for SAR Target Recognition

The local structure feature of the target in synthetic aperture radar (SAR) image and the inner correlation among multiple SAR images of the same target can effectively improve the recognition performance. In order to utilize these two kinds of information, we propose the multi-view tensor sparse representation (MTSR) model for SAR target recognition. In the proposed model, the SAR image is treated as tensor and represented by the dictionaries on each dimension of the image and corresponding sparse representation coefficient tensor (SRCT). For multi-view SAR images of one target, we design the proposed model by letting the SRCTs of those images have the same structure, which means that the non-zero elements of those SRCTs have the same coordinates. In order to solve the proposed model, we propose the joint tensor orthogonal matching pursuit (JT-OMP) algorithm to calculate the SRCTs of multiple views. JT-OMP ensures that all the SRCTs have the same structure by looking for the atoms of dictionaries that can contribute the total maximum energy for all multi-view tensors in every iteration. To achieve recognition, we construct two dictionaries for each class and compare the total sparse representation error of multi-view SAR images in each class. The experiments conducted on the moving and stationary target acquisition recognition database (MSTAR) verify the performance of the proposed algorithm.

Joint Supervised Dictionary and Classifier Learning for Multi-View SAR Image Classification

A new multi-view sparse representation classification (SRC) algorithm based on joint supervised dictionary and classifier learning (MSRC-JSDC) is proposed for synthetic aperture radar (SAR) image classification. Unlike most existing sparse representation methods for SAR image classification, MSRC-JSDC learns a supervised sparse model from training samples by utilizing sample label information, rather than directly employs a predefined one. Moreover, a supervised classifier is jointly designed during dictionary learning, which can further promote the classification performance compared with unsupervised reconstruction based classifier. In the meantime, to enhance the representation capability of the sparse model, classification error is back propagated to the dictionary learning procedure to optimize dictionary atoms. In order to extract more recognition information from collected SAR images, a multi-view strategy is applied in testing stage. A new sparse constraint is introduced into multi-view sparse representation procedure so that both inner correlation and complementary information among multiple views can be extracted. This is helpful for alleviating the influence of SAR image’s sensitivity on classification performance in such challenging scenarios as large depression variation and noise corruption. Extensive experiments on the moving and stationary target acquisition and recognition (MSTAR) database demonstrate that the proposed method is more robust and performs better than some state-of-the-art approaches.

Multiview Synthetic Aperture Radar Automatic Target Recognition Optimization: Modeling and Implementation

Multiview synthetic aperture radar (SAR) images could provide much richer information for automatic target recognition (ATR) than from a single-view image. It is desirable to find optimal SAR platform flight paths and acquire a sequence of SAR images from appropriate views, so that multiview SAR ATR can be carried out accurately and efficiently. In this paper, a novel optimization framework for multiview SAR ATR is proposed and implemented. The geometry of the multiview SAR ATR is modeled according to the recognition mission and flight environment. Then, the multiview SAR ATR is abstracted and transformed into a constrained multiobjective optimization problem with objective functions considering the tradeoffs between recognition performance and efficiency and security. A specific approach based on convolutional neural network ensemble and constrained nondominated sorting genetic algorithm II is employed to solve the multiobjective optimization, and optimal flight paths and corresponding imaging viewpoints are obtained. The SAR sensor can thus choose an applicable flight path to acquire the multiview SAR images from different tradeoff solutions according to application requirements. Finally, accurate recognition results can be obtained based on those multiview SAR images. Extensive experiments have shown the validity and superiority of the proposed optimization framework of multiview SAR ATR.

SAR Automatic Target Recognition Based on Multiview Deep Learning Framework

It is a feasible and promising way to utilize deep neural networks to learn and extract valuable features from synthetic aperture radar (SAR) images for SAR automatic target recognition (ATR). However, it is too difficult to effectively train the deep neural networks with limited raw SAR images. In this paper, we propose a new approach to do SAR ATR, in which a multiview deep learning framework was employed. Based on the multiview SAR ATR pattern, we first present a flexible mean to generate adequate multiview SAR data, which can guarantee a large amount of inputs for network training without needing many raw SAR images. Then, a unique deep convolutional neural network containing a parallel network topology with multiple inputs is adopted. The features of input SAR images from different views will be learned by the proposed network layer by layer; meanwhile, the learned features from the distinct views are fused in different layers progressively. Therefore, the proposed framework is able to achieve a superior recognition performance, and requires only a small number of raw SAR images for network training samples generation. Experimental results have shown the superiority of the proposed framework based on the Moving and Stationary Target Acquisition and Recognition data set.

Exploiting Multi-View SAR Images for Robust Target Recognition

The exploitation of multi-view synthetic aperture radar (SAR) images can effectively improve the performance of target recognition. However, due to the various extended operating conditions (EOCs) in practical applications, some of the collected views may not be discriminative enough for target recognition. Therefore, each of the input views should be examined before being passed through to multi-view recognition. This paper proposes a novel structure for multi-view SAR target recognition. The multi-view images are first classified by sparse representation-based classification (SRC). Based on the output residuals, a reliability level is calculated to evaluate the effectiveness of a certain view for multi-view recognition. Meanwhile, the support samples for each view selected by SRC collaborate to construct an enhanced local dictionary. Then, the selected views are classified by joint sparse representation (JSR) based on the enhanced local dictionary for target recognition. The proposed method can eliminate invalid views for target recognition while enhancing the representation capability of JSR. Therefore, the individual discriminability of each valid view as well as the inner correlation among all of the selected views can be exploited for robust target recognition. Experiments are conducted on the moving and stationary target acquisition recognition (MSTAR) dataset to demonstrate the validity of the proposed method.

Automatic target recognition with joint sparse representation of heterogeneous multi-view SAR images over a locally adaptive dictionary

Automatic target recognition (ATR) performance of synthetic aperture radar (SAR) is highly dependent on the sensitivity of SAR images to observing angle. Hence, jointly using of multi-view images of the same target is an efficient way to improve ATR accuracy, since multi-view images carry more correlated information than single-view image. Taking into account heterogeneous multi-views with random not uniform observing interval, an ATR approach with joint sparse representation over a locally adaptive dictionary is investigated in this paper. The first step is to establish a locally adaptive dictionary using sparse representation (SR) after training samples dimension reduction process by Independent and Identically Distributed (IID) Gaussian random project matrix. The locally adaptive dictionary is able to alleviate the limitation of target pose by adjusting the use of information in images and between images with the interval changing. Then heterogeneous multi-view test samples are re-presented by selecting atoms from the locally adaptive dictionary using joint sparse representation (JSR). In such way, high recognition accuracy is guaranteed by combination of more target information and adjustment of the inter-correlation information guarantee. Experiments based on the Moving and Stationary Target Acquisition and Recognition database verify the performance of the proposed algorithm.