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Remote Sens. 2017, 9(11), 1150;

Exploiting Multi-View SAR Images for Robust Target Recognition

Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China
Author to whom correspondence should be addressed.
Academic Editor: Qi Wang
Received: 19 September 2017 / Revised: 25 October 2017 / Accepted: 7 November 2017 / Published: 9 November 2017
(This article belongs to the Section Remote Sensing Image Processing)
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The exploitation of multi-view synthetic aperture radar (SAR) images can effectively improve the performance of target recognition. However, due to the various extended operating conditions (EOCs) in practical applications, some of the collected views may not be discriminative enough for target recognition. Therefore, each of the input views should be examined before being passed through to multi-view recognition. This paper proposes a novel structure for multi-view SAR target recognition. The multi-view images are first classified by sparse representation-based classification (SRC). Based on the output residuals, a reliability level is calculated to evaluate the effectiveness of a certain view for multi-view recognition. Meanwhile, the support samples for each view selected by SRC collaborate to construct an enhanced local dictionary. Then, the selected views are classified by joint sparse representation (JSR) based on the enhanced local dictionary for target recognition. The proposed method can eliminate invalid views for target recognition while enhancing the representation capability of JSR. Therefore, the individual discriminability of each valid view as well as the inner correlation among all of the selected views can be exploited for robust target recognition. Experiments are conducted on the moving and stationary target acquisition recognition (MSTAR) dataset to demonstrate the validity of the proposed method. View Full-Text
Keywords: synthetic aperture radar (SAR); multi-view recognition; sparse representation-based classification (SRC); enhanced local dictionary; joint sparse representation (JSR); MSTAR synthetic aperture radar (SAR); multi-view recognition; sparse representation-based classification (SRC); enhanced local dictionary; joint sparse representation (JSR); MSTAR

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Ding, B.; Wen, G. Exploiting Multi-View SAR Images for Robust Target Recognition. Remote Sens. 2017, 9, 1150.

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