论文类型：期刊论文
发表刊物：IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
收录刊物：Scopus、EI、SCIE
卷号：14
期号：6
页面范围：791-795
ISSN号：1545-598X
关键字：Analysis dictionary; coupled dictionary learning (CDL); shared dictionary; synthetic aperture radar (SAR) image; target recognition
摘要：In this letter, we propose a novel classification strategy called the coupled dictionary learning for target recognition in synthetic aperture radar (SAR) images. First, we train structured synthesis dictionaries to reflect the difference among each category. Second, we introduce a shared dictionary to reduce the effect of common features, such as the high similarity caused by specular reflection. Finally, we use the analysis dictionary to improve the efficiency of recognition by eliminating the constraint of the l(0)-norm or l(1)-norm of sparse code. Experimental results on Moving and Stationary Target Acquisition and Recognition data set indicate that our method can achieve better performance in SAR target recognition than the state-of-the-art methods, such as tritask joint sparse representation and CKLR. Especially, this method can be more robust when the depressions have obvious changes.