Joint sparse representation based automatic target recognition in SAR images

Haichao Zhang; Nasser M. Nasrabadi; Thomas S. Huang; Yanning Zhang

doi:10.1117/12.883665

4 May 2011 Joint sparse representation based automatic target recognition in SAR images

Haichao Zhang, Nasser M. Nasrabadi, Thomas S. Huang, Yanning Zhang

Proceedings Volume 8051, Algorithms for Synthetic Aperture Radar Imagery XVIII; 805112 (2011) https://doi.org/10.1117/12.883665
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

Haichao Zhang, Nasser M. Nasrabadi, Thomas S. Huang, and Yanning Zhang "Joint sparse representation based automatic target recognition in SAR images", Proc. SPIE 8051, Algorithms for Synthetic Aperture Radar Imagery XVIII, 805112 (4 May 2011); https://doi.org/10.1117/12.883665

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