A Novel Sense-through-Foliage Target Recognition Method Based on Sparse Representation

2013 ◽  
Author(s):  
Shijun Zhai ◽  
Ting Jiang ◽  
Jingjing Huang
Keyword(s):  
Sparse Representation ◽  
Target Recognition ◽  
Recognition 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.

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

The Cat's Eye Effect Target Recognition Method Based on Visual Attention

2019 ◽  
Vol 28 (5) ◽  
pp. 1080-1086 ◽  
Author(s):  
Xingbin Wang ◽  
Jun Zhang ◽  
Shuaihui Wang
Keyword(s):  
Visual Attention ◽  
Target Recognition ◽  
Recognition Method

scholarly journals A Deformation Robust ISAR Image Satellite Target Recognition Method Based on PT-CCNN

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 23432-23453
Author(s):  
Wang Lu ◽  
Yasheng Zhang ◽  
Canbin Yin ◽  
Caiyong Lin ◽  
Can Xu ◽  
...  
Keyword(s):  
Target Recognition ◽  
Recognition Method ◽  
Image Satellite
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