Synthetic aperture radar target recognition based on adaptive decision fusion of multiple views

For the synthetic aperture radar (SAR) automatic target recognition problem, we propose a multiview method based on adaptive decision fusion. Considering the possible independence and correlation among multiview SAR images, a clustering algorithm is first performed to divide them into different view sets. In each view set, the included SAR images are assumed to be highly correlated, which can be effectively classified through joint sparse representation. For the output decisions from different view sets, they are assumed to be relatively independent and contribute disproportionally to the recognition result. Therefore, based on the decisions of individual view sets, adaptive weights are decided based on Shannon entropy theory, which are used to linearly fuse the multiple decisions to determine the target label. The MSTAR dataset is employed in experiments, in which both the standard operating condition and some representative extended operating conditions are set up. The performance of the proposed method is studied in comparison with representative state-of-the-art methods.