SAR Target Recognition Based on Joint Sparse Representation of Complementary Features

Considering the defaults in synthetic aperture radar (SAR) image feature extraction, an SAR target recognition method based on non-subsampled Shearlet transform (NSST) was proposed with application to target recognition. NSST was used to decompose an SAR image into multilevel representations. These representations were translation-invariant, and they could well reflect the dominant and detailed properties of the target. During the machine learning classification stage, the joint sparse representation was employed to jointly represent the multilevel representations. The joint sparse representation could represent individual components independently while considering the inner correlations between different components. Therefore, the precision of joint representation could be enhanced. Finally, the target label of the test sample was determined according to the overall reconstruction error. Experiments were conducted on the MSTAR dataset to examine the proposed method, and the results confirmed its validity and robustness under the standard operating condition, configuration variance, depression angle variance, and noise corruption.

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Joint sparse representation for multi-resolution representations of SAR images with application to target recognition

Journal of Electromagnetic Waves and Applications ◽

10.1080/09205071.2018.1436005 ◽

2018 ◽

Vol 32 (11) ◽

pp. 1342-1353 ◽

Cited By ~ 5

Author(s):

Zhenyu Zhang ◽

Sikai Liu

Keyword(s):

Sparse Representation ◽

Target Recognition ◽

Sar Images ◽

Joint Sparse Representation

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

Scientific Programming ◽

10.1155/2021/1258219 ◽

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.

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