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Proceedings Paper

Automatic target recognition using group-structured sparse representation
Author(s): Bo Sun; Xuewen Wu; Jun He; Xiaoming Zhu; Chao Chen
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Paper Abstract

Sparse representation classification method has been increasingly used in the fields of computer vision and pattern analysis, due to its high recognition rate, little dependence on the features, robustness to corruption and occlusion, and etc. However, most of these existing methods aim to find the sparsest representations of the test sample y in an overcomplete dictionary, which do not particularly consider the relevant structure between the atoms in the dictionary. Moreover, sufficient training samples are always required by the sparse representation method for effective recognition. In this paper we formulate the classification as a group-structured sparse representation problem using a sparsity-inducing norm minimization optimization and propose a novel sparse representation-based automatic target recognition (ATR) framework for the practical applications in which the training samples are drawn from the simulation models of real targets. The experimental results show that the proposed approach improves the recognition rate of standard sparse models, and our system can effectively and efficiently recognize targets under real environments, especially, where the good characteristics of the sparse representation based classification method are kept.

Paper Details

Date Published: 13 June 2014
PDF: 7 pages
Proc. SPIE 9090, Automatic Target Recognition XXIV, 90900N (13 June 2014); doi: 10.1117/12.2049514
Show Author Affiliations
Bo Sun, Beijing Normal Univ. (China)
Xuewen Wu, Beijing Normal Univ. (China)
Jun He, Beijing Normal Univ. (China)
Xiaoming Zhu, Beijing Normal Univ. (China)
Chao Chen, Naval Academy of Armament (China)

Published in SPIE Proceedings Vol. 9090:
Automatic Target Recognition XXIV
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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