Decision fusion of sparse representation and support vector machine for SAR image target recognition
We propose a decision fusion method of Sparse Representation (SR) and Support Vector Machine (SVM) for Synthetic Aperture Radar (SAR) image target recognition in this paper. First, a fast SR classifier (FSR-C) with Matching Pursuit (MP) solution is proposed. In the FSR-C, the dictionary is composed of training images. Just one nonzero element in SR coefficient of the testing image is found out based on MP, and the testing image is classified through the location of the nonzero element. To further improve the recognition accuracy, the SVM classifier (SVM-C) is selected. In SVM-C, PCA feature is extracted, and for seeking the linear separating hyperplane, the RBF kernel function is used in mapping the training vectors into high dimensional space. The results of the FSR-C and the SVM-C are fused obeying Bayesian rule to make the decision. The Moving and Stationary Target Acquisition and Recognition (MSTAR) SAR image database is used to test the performance of the proposed method. The experimental results show that the FSR-C can predict testing SAR images with considerable recognition accuracy and high real-time ability, and the decision fusion recognition method can improve the recognition accuracy and still be fast.
decision fusion sparse representation sar target recognition svm