SAR image target feature extraction based on KSVD and PCA

In this paper a Synthetic Aperture Radar (SAR) image target extraction method based on Kernel Singular Value Decomposition (KSVD) and Principal Component Analysis (PCA) is proposed. First it acquires the nonlinear algebraic feature of SAR images by performing KSVD; then obtains the last discriminating feature using PCA; and finally the nearest neighbor classifier is used for recognition. The KSVD and PCA are carried out on MSTAR tank dataset in comparison with traditional PCA, SVD, KSVD and Kernel Principal Component Analysis (KPCA). Experiment results demonstrate that the KSVD and PCA method proposed in this paper is effective for SAR image target feature extraction. Not only the right recognition rate is higher of the new method but also it is not sensitive to target azimuth.