An effective method for SAR automatic target recognition

Authors:
Ying Li;Hongli Gong
Affiliations:
School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China;School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China
Venue:
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Year:
2010

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Abstract

Since synthetic aperture radar (SAR) images are very sensitive to the pose variation of targets, SAR automatic target recognition (ATR) is a well-known very challenging problem. This paper introduces an effective method for SAR ATR by using a combination of kernel singular value decomposition (KSVD) and principal component analysis (PCA) for feature extraction and the nearest neighbor classifier (NNC) for classification. Experiments are carried out on the Moving and Stationary Target Acquisition and Recognition (MSTAR) public database to evaluate the performance of the proposed method in comparison with the traditional PCA, singular value decomposition (SVD), kernel PCA (KPCA) and KSVD. The results demonstrate that the proposed method performs much better than the other methods with a right recognition rate up to 95.75%.