RETRACTED: Automatic Target Recognition of SAR Images Using Collaborative Representation (Retracted Article)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
[21]   Improving SAR Automatic Target Recognition Models With Transfer Learning From Simulated Data [J].
Malmgren-Hansen, David ;
Kusk, Anders ;
Dall, Jorgen ;
Nielsen, Allan Aasbjerg ;
Engholm, Rasmus ;
Skriver, Henning .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2017, 14 (09) :1484-1488
[22]   A Gradually Distilled CNN for SAR Target, Recognition [J].
Min, Rui ;
Lan, Hai ;
Cao, Zongjie ;
Cui, Zongyong .
IEEE ACCESS, 2019, 7 :42190-42200
[23]   Deep convolutional neural networks for ATR from SAR imagery [J].
Morgan, David A. E. .
ALGORITHMS FOR SYNTHETIC APERTURE RADAR IMAGERY XXII, 2015, 9475
[24]   Attributed scattering centers for SAR ATR [J].
Potter, LC ;
Moses, RL .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 1997, 6 (01) :79-91
[25]   Binary Morphological Filtering of Dominant Scattering Area Residues for SAR Target Recognition [J].
Shan, Chao ;
Huang, Bin ;
Li, Minggao .
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2018, 2018
[26]  
Singh IP, 2008, 2008 10TH IEEE INTERNATIONAL CONFERENCE ON E-HEALTH NETWORKING, APPLICATIONS AND SERVICES, P1, DOI 10.1109/HEALTH.2008.4600098
[27]   Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set [J].
Song, Haibo ;
Ji, Kefeng ;
Zhang, Yunshu ;
Xing, Xiangwei ;
Zou, Huanxin .
APPLIED SCIENCES-BASEL, 2016, 6 (01)
[28]   Target recognition of SAR images by partially matching of target outlines [J].
Tan, Jian ;
Fan, Xiangtao ;
Wang, Shenghua ;
Ren, Yingchao ;
Guo, Changshun ;
Liu, Jian ;
Li, Jing ;
Zhan, Qin .
JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS, 2019, 33 (07) :865-881
[29]  
Thiagarajan J.J., 2010, P 4 INT S COMM CONTR, P1, DOI [DOI 10.1109/ISCCSP.2010.5463416, 10.1109/ISCCSP.2010.5463416, https://doi.org/10.1109/ISCCSP.2010.5463416]
[30]   Target recognition in SAR images with support vector machines (SVM) [J].
Tison, Celine ;
Pourthie, Nadine ;
Souyris, Jean-Claude .
IGARSS: 2007 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-12: SENSING AND UNDERSTANDING OUR PLANET, 2007, :456-459