Target Recognition of Synthetic Aperture Radar Images Based on Two-Phase Sparse Representation

A synthetic aperture radar (SAR) target recognition method is proposed via linear representation over the global and local dictionaries. The collaborative representation is performed on the local dictionary, which comprises of training samples from a single class. Then, the reconstruction errors as for representing the test sample reflect the absolute representation capabilities of different training classes. Accordingly, the target label can be directly decided when one class achieves a notably lower reconstruction error than the others. Otherwise, several candidate classes with relatively low reconstruction errors are selected as the candidate classes to form the global dictionary, based on which the sparse representation-based classification (SRC) is performed. SRC also produces the reconstruction errors of the candidate classes, which reflect their relative representation capabilities for the test sample. As a comprehensive consideration, the reconstruction errors from the collaborative representation and SRC are fused for decision-making. Therefore, the proposed method could inherit the high efficiency of the collaborative representation. In addition, the selection of the candidate training classes also relieves the computational burden during SRC. By combining the absolute and relative representation capabilities, the final classification accuracy can also be improved. During the experimental evaluation, the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is employed to test the proposed method under several different operating conditions. The proposed method is compared with some other SAR target recognition methods simultaneously. The results show the superior performance of the proposed method.