Classification via Sparse Representation of Steerable Wavelet Frames on Grassmann Manifold: Application to Target Recognition in SAR Image

2017 ◽  
Vol 26 (6) ◽  
pp. 2892-2904 ◽  
Author(s):  
Ganggang Dong ◽  
Gangyao Kuang ◽  
Na Wang ◽  
Wei Wang
Keyword(s):  
Sparse Representation ◽  
Grassmann Manifold ◽  
Target Recognition ◽  
Sar Image ◽  
Wavelet Frames

scholarly journals Feature Extraction Based on Non-Subsampled Shearlet Transform (NSST) with Application to SAR Image Data

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Huijie Ding ◽  
Arthur K. L. Lin
Keyword(s):  
Feature Extraction ◽  
Sparse Representation ◽  
Target Recognition ◽  
Image Feature ◽  
Sar Image ◽  
Translation Invariant ◽  
Shearlet Transform ◽  
Machine Learning Classification ◽  
Joint Sparse Representation ◽  
Multilevel Representations

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.

scholarly journals SAR Image Target Recognition Based on Monogenic Signal and Sparse Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
XiuXia Ji ◽  
Yinan Sun
Keyword(s):  
Sparse Representation ◽  
Target Recognition ◽  
Sar Image ◽  
Recognition Method ◽  
Average Correlation ◽  
Monogenic Signal ◽  
New Approach ◽  
Training Samples ◽  
Overcomplete Dictionary ◽  
Feature Information

It is necessary to recognize the target in the situation of military battlefield monitoring and civilian real-time monitoring. Sparse representation-based SAR image target recognition method uses training samples or feature information to construct an overcomplete dictionary, which will inevitably affect the recognition speed. In this paper, a method based on monogenic signal and sparse representation is presented for SAR image target recognition. In this method, the extended maximum average correlation height filter is used to train the samples and generate the templates. The monogenic features of the templates are extracted to construct subdictionaries, and the subdictionaries are combined to construct a cascade dictionary. Sparse representation coefficients of the testing samples over the cascade dictionary are calculated by the orthogonal matching tracking algorithm, and recognition is realized according to the energy of the sparse coefficients and voting recognition. The experimental results suggest that the new approach has good results in terms of recognition accuracy and recognition time.

Sar Image Target Recognition Method Based On Sparse Representation Of Local Dictionary

2021 ◽  
pp. 104070
Author(s):  
Han Hongliang ◽  
Bai Yonglei ◽  
Lu Wei ◽  
Feng Fan ◽  
Wang Jianhua
Keyword(s):  
Sparse Representation ◽  
Target Recognition ◽  
Sar Image ◽  
Recognition Method
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