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Vol. 161, Issue 12, December 2013, pp. 67-73

 

Bullet

 

Sar Images Target Recognition Based on Sparse Representation
 
1, 2 Xiu Xia JI, 1 Gong ZHANG

1 College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu China

 2 Jincheng college, Nanjing University of Aeronautics and Astronautics, Nanjing 211156, Jiangsu China

1 Tel.: 13390753980

1 E-mail: 104066779@qq.com

 

Received: 18 September 2013   /Accepted: 22 November 2013   /Published: 30 December 2013

Digital Sensors and Sensor Sysstems

 

Abstract: Sparsity is a significant features of synthetic aperture radar images. A method based on sparse representation is presented for synthetic aperture radar target recognition after analyzing the statistical characteristic of synthetic aperture radar images in order to solve the high dimensional problem based on sparse representation in pixel domain. It trains samples and generates templets using the extended maximum average correlation height filter, extracts the template's generalized two-dimensional principal component analyze features to form an over-complete dictionary, sparse representation coefficient of the test sample's feature is computed base on the optimal dictionary. Classification and recognition is realized according to the energy of coefficient. Experimental results based on synthetic aperture radar images in MSTAR database show that the proposed method has lower complexity and short recognition time, it is a feasible and effective method for synthetic aperture radar images target recognition.

 

Keywords: Sparse representation, Synthetic aperture radar, Extended maximum average correlation height, Generalized two-dimensional principal component analyze, Target recognition.

 

 

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