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Proceedings Paper

Joint sparse representation based automatic target recognition in SAR images
Author(s): Haichao Zhang; Nasser M. Nasrabadi; Thomas S. Huang; Yanning Zhang
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Paper Abstract

In this paper, we introduce a novel joint sparse representation based automatic target recognition (ATR) method using multiple views, which can not only handle multi-view ATR without knowing the pose but also has the advantage of exploiting the correlations among the multiple views for a single joint recognition decision. We cast the problem as a multi-variate regression model and recover the sparse representations for the multiple views simultaneously. The recognition is accomplished via classifying the target to the class which gives the minimum total reconstruction error accumulated across all the views. Extensive experiments have been carried out on Moving and Stationary Target Acquisition and Recognition (MSTAR) public database to evaluate the proposed method compared with several state-of-the-art methods such as linear Support Vector Machine (SVM), kernel SVM as well as a sparse representation based classifier. Experimental results demonstrate that the effectiveness as well as robustness of the proposed joint sparse representation ATR method.

Paper Details

Date Published: 5 May 2011
PDF: 12 pages
Proc. SPIE 8051, Algorithms for Synthetic Aperture Radar Imagery XVIII, 805112 (5 May 2011); doi: 10.1117/12.883665
Show Author Affiliations
Haichao Zhang, Northwestern Polytechnical Univ. (China)
Univ. of Illinois at Urbana-Champaign (United States)
Nasser M. Nasrabadi, U.S. Army Research Lab. (United States)
Thomas S. Huang, Univ. of Illinois at Urbana-Champaign (United States)
Yanning Zhang, Northwestern Polytechnical Univ. (China)


Published in SPIE Proceedings Vol. 8051:
Algorithms for Synthetic Aperture Radar Imagery XVIII
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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