SAR Target Recognition Based on Joint Representation of Multimode Representations

For the problems of feature extraction and decision making in synthetic aperture radar (SAR) image target recognition, a method based on multimode clustering and decision fusion is proposed. The bidimensional variational mode decomposition (BVMD) is used to decompose the SAR image to obtain multiple modes, which provide multilevel descriptions of the target characteristics. Clustering is performed based on the intrinsic correlation of multiple modes, and several subsets with different modes are selected. Based on the joint sparse representation (JSR), each mode subset is classified, and the corresponding reconstruction error vector is obtained. The linear weighted fusion is employed to fuse the results from different mode subsets. Finally, a decision is made based on the fused results. Experiments are carried out based on the MSTAR dataset. The results show the effectiveness of the method under the standard operating condition (SOC) and robustness under extended operating conditions (EOCs).