RETRACTED: Automatic Target Recognition of SAR Images Using Collaborative Representation (Retracted Article)

被引:3
作者
Hu, Jinge [1 ]
机构
[1] Chongqing Three Gorges Univ, Chongqing 404100, Peoples R China
关键词
CONVOLUTIONAL NEURAL-NETWORK; JOINT SPARSE REPRESENTATION; SUPPORT VECTOR MACHINES;
D O I
10.1155/2022/3100028
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Synthetic aperture radar (SAR) automatic target recognition (ATR) is one of the key technologies for SAR image interpretation. This paper proposes a SAR target recognition method based on collaborative representation-based classification (CRC). The collaborative coding adopts the global dictionary constructed by training samples of all categories to optimally reconstruct the test samples and determines the target category according to the reconstruction error of each category. Compared with the sparse representation methods, the collaborative representation strategy can improve the representation ability of a small number of training samples for test samples. For SAR target recognition, the resources of training samples are very limited. Therefore, the collaborative representation is more suitable. Based on the MSTAR dataset, the experiments are carried out under a variety of conditions and the proposed method is compared with other classifiers. Experimental results show that the proposed method can achieve superior recognition performance under the standard operating condition (SOC), configuration variances, depression angle variances, and a small number of training samples, which proves its effectiveness.
引用
收藏
页数:7
相关论文
共 42 条
[1]   Automatic target recognition of synthetic aperture radar (SAR) images based on optimal selection of Zernike moments features [J].
Amoon, Mehdi ;
Rezai-rad, Gholam-ali .
IET COMPUTER VISION, 2014, 8 (02) :77-85
[2]   Bidimensional Empirical Mode Decomposition for SAR Image Feature Extraction With Application to Target Recognition [J].
Chang, Ming ;
You, Xuqun ;
Cao, Zhengyang .
IEEE ACCESS, 2019, 7 :135720-135731
[3]   Target Classification Using the Deep Convolutional Networks for SAR Images [J].
Chen, Sizhe ;
Wang, Haipeng ;
Xu, Feng ;
Jin, Ya-Qiu .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2016, 54 (08) :4806-4817
[4]   Target recognition in synthetic aperture radar images via non-negative matrix factorisation [J].
Cui, Zongyong ;
Cao, Zongjie ;
Yang, Jianyu ;
Feng, Jilan ;
Ren, Hongliang .
IET RADAR SONAR AND NAVIGATION, 2015, 9 (09) :1376-1385
[5]  
Demirhan ME, 2016, 2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU), P1581, DOI 10.1109/SIU.2016.7496056
[6]   Sparsity constraint nearest subspace classifier for target recognition of SAR images [J].
Ding, Baiyuan ;
Wen, Gongjian .
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2018, 52 :170-176
[7]   Target Recognition in Synthetic Aperture Radar Images via Matching of Attributed Scattering Centers [J].
Ding, Baiyuan ;
Wen, Gongjian ;
Huang, Xiaohong ;
Ma, Conghui ;
Yang, Xiaoliang .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2017, 10 (07) :3334-3347
[8]   Target recognition in synthetic aperture radar images using binary morphological operations [J].
Ding, Baiyuan ;
Wen, Gongjian ;
Ma, Conghui ;
Yang, Xiaoliang .
JOURNAL OF APPLIED REMOTE SENSING, 2016, 10
[9]   A robust similarity measure for attributed scattering center sets with application to SAR ATR [J].
Ding, Baiyuan ;
Wen, Gongjian ;
Zhong, Jinrong ;
Ma, Conghui ;
Yang, Xiaoliang .
NEUROCOMPUTING, 2017, 219 :130-143
[10]   Convolutional Neural Network With Data Augmentation for SAR Target Recognition [J].
Ding, Jun ;
Chen, Bo ;
Liu, Hongwei ;
Huang, Mengyuan .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2016, 13 (03) :364-368