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Indexed by:期刊论文

Date of Publication:2017-06-01

Journal:IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

Included Journals:SCIE、EI、Scopus

Volume:14

Issue:6

Page Number:791-795

ISSN No.:1545-598X

Key Words:Analysis dictionary; coupled dictionary learning (CDL); shared dictionary; synthetic aperture radar (SAR) image; target recognition

Abstract: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.