Sub-dictionary Based Joint Sparse Representation for Multi-aspect SAR Automatic Target Recognition

被引：5

作者：

Xu, Liyuan ^{[1
]}

Cao, Zongjie ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Elect Engn, Chengdu 610054, Peoples R China

来源：

PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON COMMUNICATIONS, SIGNAL PROCESSING, AND SYSTEMS | 2015年 / 322卷

关键词：

SAR ATR; Multi-aspect; Sparse representation; Joint sparse representation; IID Gaussian random projection;

D O I：

10.1007/978-3-319-08991-1_18

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Joint sparse representation (JSR) is mostly used in face recognition area. While in this paper, JSR is adopted in the area of SAR automatic target recognition (ATR). In our method, the whole training dictionary is divided into several sub-dictionaries, according to the label of training samples. And classification is based on the minimum representation error criterion. Independent and identically distributed (IID) Gaussian random projection is used to extract feature of SAR images. Experiments are carried out on moving and stationary target acquisition and recognition (MSTAR) public database. Experiments results show that recognition rates can be improved by our method, by combining more useful information and reducing interference information of target.

引用

页码：167 / 175

页数：9

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