SAR vehicle classification based on sparse representation with aspect angle constraint

Xiangwei Xing; Kefeng Ji; Huanxin Zou; Jixiang Sun

doi:10.1117/12.2031412

26 October 2013 SAR vehicle classification based on sparse representation with aspect angle constraint

Xiangwei Xing, Kefeng Ji, Huanxin Zou, Jixiang Sun

Proceedings Volume 8918, MIPPR 2013: Automatic Target Recognition and Navigation; 89180K (2013) https://doi.org/10.1117/12.2031412
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China

Abstract

As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation has attracted much attention in target classification recently. In this paper, we develop a new SAR vehicle classification method based on sparse representation, in which the correlation between the vehicle’s aspect angle and the sparse representation coefficients is exploited. The detail procedure presented in this paper can be summarized as follows. Initially, the sparse coefficient vector of a test sample is solved by sparse representation algorithm with a pixel based dictionary. Then the coefficient vector is projected onto a sparser one with the constraint of vehicle’s aspect angle. Finally, the vehicle is classified to a certain category that minimizes the reconstruct error with the sparse coefficient vector. We present promising results of applying the proposed method to the MSTAR dataset.

Citation Download Citation

Xiangwei Xing, Kefeng Ji, Huanxin Zou, and Jixiang Sun "SAR vehicle classification based on sparse representation with aspect angle constraint", Proc. SPIE 8918, MIPPR 2013: Automatic Target Recognition and Navigation, 89180K (26 October 2013); https://doi.org/10.1117/12.2031412

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