SAR Target Recognition Based on Joint Representation of Multimode Representations

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Youchun Qiu
Keyword(s):  
Target Recognition ◽  
Decision Fusion ◽  
Reconstruction Error ◽  
Operating Conditions ◽  
Sar Image ◽  
Multiple Modes ◽  
Weighted Fusion ◽  
Mode Decomposition ◽  
Intrinsic Correlation ◽  
Target Characteristics

For the problems of feature extraction and decision making in synthetic aperture radar (SAR) image target recognition, a method based on multimode clustering and decision fusion is proposed. The bidimensional variational mode decomposition (BVMD) is used to decompose the SAR image to obtain multiple modes, which provide multilevel descriptions of the target characteristics. Clustering is performed based on the intrinsic correlation of multiple modes, and several subsets with different modes are selected. Based on the joint sparse representation (JSR), each mode subset is classified, and the corresponding reconstruction error vector is obtained. The linear weighted fusion is employed to fuse the results from different mode subsets. Finally, a decision is made based on the fused results. Experiments are carried out based on the MSTAR dataset. The results show the effectiveness of the method under the standard operating condition (SOC) and robustness under extended operating conditions (EOCs).

scholarly journals Target Recognition of SAR Images Based on Complex Bidimensional Empirical Mode Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xinying Miao ◽  
Yunlong Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Target Recognition ◽  
Test Sample ◽  
Operating Conditions ◽  
Sar Image ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Standard Operating Condition ◽  
Bidimensional Empirical Mode Decomposition

A target recognition method for synthetic aperture radar (SAR) image based on complex bidimensional empirical mode decomposition (C-BEMD) is proposed. C-BEMD is used to decompose the original SAR image to obtain multilevel complex bidimensional intrinsic mode functions (BIMF), which reflect the two-dimensional time-frequency characteristics of the target. In the classification stage, the decomposed multilevel BIMFs are represented using the multitask sparse representation. Finally, the target category of the test sample is determined according to the reconstruction errors related to different training classes. In the experiment, the standard operating condition (SOC) and extended operating conditions (EOC) are designed based on the MSTAR dataset to test and verify the proposed method. The results confirm the effectiveness and robustness of the method.

scholarly journals Combination of Joint Representation and Adaptive Weighting for Multiple Features with Application to SAR Target Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Liqun Yu ◽  
Lu Wang ◽  
Yongxing Xu
Keyword(s):  
Target Recognition ◽  
Nonnegative Matrix ◽  
Decision Fusion ◽  
Reconstruction Error ◽  
Operating Conditions ◽  
Stationary Target ◽  
Noise Interference ◽  
Adaptive Weighting ◽  
Joint Classification ◽  
Sar Target Recognition

For the synthetic aperture radar (SAR) target recognition problem, a method combining multifeature joint classification and adaptive weighting is proposed with innovations in fusion strategies. Zernike moments, nonnegative matrix factorization (NMF), and monogenic signal are employed as the feature extraction algorithms to describe the characteristics of original SAR images with three corresponding feature vectors. Based on the joint sparse representation model, the three types of features are jointly represented. For the reconstruction error vectors from different features, an adaptive weighting algorithm is used for decision fusion. That is, the weights are adaptively obtained under the framework of linear fusion to achieve a good fusion result. Finally, the target label is determined according to the fused error vector. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset under the standard operating condition (SOC) and four extended operating conditions (EOC), i.e., configuration variants, depression angle variances, noise interference, and partial occlusion. The results verify the effectiveness and robustness of the proposed method.

scholarly journals A SAR Target Recognition Method Based on Decision Fusion of Multiple Features and Classifiers

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhengwu Lu ◽  
Guosong Jiang ◽  
Yurong Guan ◽  
Qingdong Wang ◽  
Jianbo Wu
Keyword(s):  
Sparse Representation ◽  
Target Recognition ◽  
Decision Fusion ◽  
Operating Conditions ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Recognition Method ◽  
Multiple Features ◽  
Decision Variables ◽  
Sar Target Recognition

A synthetic aperture radar (SAR) target recognition method combining multiple features and multiple classifiers is proposed. The Zernike moments, kernel principal component analysis (KPCA), and monographic signals are used to describe SAR image features. The three types of features describe SAR target geometric shape features, projection features, and image decomposition features. Their combined use can effectively enhance the description of the target. In the classification stage, the support vector machine (SVM), sparse representation-based classification (SRC), and joint sparse representation (JSR) are used as the classifiers for the three types of features, respectively, and the corresponding decision variables are obtained. For the decision variables of the three types of features, multiple sets of weight vectors are used for weighted fusion to determine the target label of the test sample. In the experiment, based on the MSTAR dataset, experiments are performed under standard operating condition (SOC) and extended operating conditions (EOCs). The experimental results verify the effectiveness, robustness, and adaptability of the proposed method.

Target Recognition in SAR Image by Joint Classification of Target Region and Shadow

2019 ◽  
Vol 12 (4) ◽  
pp. 347-354
Author(s):  
Yongpeng Tao ◽  
Yu Jing ◽  
Cong Xu
Keyword(s):  
Learning Algorithm ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Operating Conditions ◽  
Sar Image ◽  
Stationary Target ◽  
Target Region ◽  
Feature Vectors ◽  
Joint Classification

Background: A synthetic aperture radar (SAR) automatic target recognition (ATR) method is proposed in this paper via the joint classification of the target region and shadow. Methods: The elliptical Fourier descriptors (EFDs) are used to describe the target region and shadow extracted from the original SAR image. In addition, the relative positions between the target region and shadow are represented by a constructed feature vector. The three feature vectors complement each other to provide more comprehensive descriptions of the target’s physical properties, e.g., sizes and shape. In the classification stage, the three feature vectors are jointly classified based on the joint sparse representation (JSR). JSR is a multi-task learning algorithm, which can not only represent each component properly but also exploit the inner correlations of different components. Finally, the target type is determined to the class with the minimum reconstruction error. Results: Experiments have been conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The proposed method achieves a high recognition accuracy of 96.86% for 10-class recognition problem under the standard operating condition (SOC). Moreover, robustness of the proposed method is also superior over the reference methods under the extended operating conditions (EOCs) like configuration variance, depression angle variance, and noise corruption. Conclusion: Therefore, the effectiveness and robustness of the proposed method can be quantitatively demonstrated by the experimental results.

scholarly journals Improving SAR Target Recognition Performance Using Multiple Preprocessing Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Qinmin Ma
Keyword(s):  
Image Enhancement ◽  
Target Recognition ◽  
Recognition Performance ◽  
Sar Image ◽  
Monogenic Signal ◽  
Stationary Target ◽  
Target Segmentation ◽  
Target Characteristics ◽  
Subsequent Target ◽  
Sar Target Recognition

The synthetic aperture radar (SAR) image preprocessing techniques and their impact on target recognition performance are researched. The performance of SAR target recognition is improved by composing a variety of preprocessing techniques. The preprocessing techniques achieve the effects of suppressing background redundancy and enhancing target characteristics by processing the size and gray distribution of the original SAR image, thereby improving the subsequent target recognition performance. In this study, image cropping, target segmentation, and image enhancement algorithms are used to preprocess the original SAR image, and the target recognition performance is effectively improved by combining the above three preprocessing techniques. On the basis of image enhancement, the monogenic signal is used for feature extraction and then the sparse representation-based classification (SRC) is used to complete the decision. The experiments are conveyed on the moving and stationary target acquisition and recognition (MSTAR) dataset, and the results prove that the combination of multiple preprocessing techniques can effectively improve the SAR target recognition performance.

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