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%.
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Li, Y., Gong, H. (2010). An Effective Method for SAR Automatic Target Recognition. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2010. Lecture Notes in Computer Science(), vol 6319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16530-6_52
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DOI: https://doi.org/10.1007/978-3-642-16530-6_52
Publisher Name: Springer, Berlin, Heidelberg
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