Sparse Weighting for Pyramid Pooling-Based SAR Image Target Recognition

Abstract

In this study, a novel feature learning method for synthetic aperture radar (SAR) image automatic target recognition is presented. It is based on spatial pyramid matching (SPM), which represents an image by concatenating the pooling feature vectors that are obtained from different resolution sub-regions. This method exploits the dependability of obtaining the weighted pooling features generated from SPM sub-regions. The dependability is determined by the residuals obtained from sparse representation. This method aims at enhancing the weights of the pooling features generated in the sub-regions located in the target and suppressing the weights of the background. The feature representation for SAR image target recognition is discriminative and robust to speckle noise and background clutter. Experiments performed on the Moving and Stationary Target Acquisition and Recognition public dataset prove the advantageous performance of the presented algorithm over several state-of-the-art methods.