SAR Target Configuration Recognition via Discriminative Statistical Dictionary Learning

The establishment of the dictionary is of crucial importance to sparse representation (SR) based algorithms. It has been proved that learned dictionaries are more compact and have better performance than predefined ones. A dictionary learning (DL) algorithm is proposed for synthetic aperture radar (SAR) target configuration recognition in a statistical way with discriminative power embedded in under the SR framework, which is named as discriminative statistical DL (DSDL) in this paper. A better description of the SAR image is obtained from the statistical standpoint and, thus, improving the robustness of the proposed algorithm. To make the learned dictionary more discriminative, the dictionary is finally determined while minimizing differences within the same target configuration and, meanwhile, maximizing differences among different configurations. The proposed DSDL algorithm can provide dictionaries for SR-based recognition algorithms. Experimental results on the moving and stationary target acquisition and recognition database validate the effectiveness and robustness of the proposed DSDL-SR recognition algorithm. A comparison with other recognition techniques further demonstrates its superiority.