SAR target configuration recognition via structure preserving dictionary learning

Abstract

Learned dictionaries have been validated to perform better than predefined ones in many application areas. Focusing on synthetic aperture radar (SAR) images, a structure preserving dictionary learning (SPDL) algorithm, which can capture and preserve the local and distant structures of the datasets for SAR target configuration recognition is proposed in this paper. Due to the target aspect angle sensitivity characteristic of SAR images, two structure preserving factors are embedded into the proposed SPDL algorithm. One is constructed to preserve the local structure of the datasets, and the other one is established to preserve the distant structure of the datasets. Both the local and distant structures of the datasets are preserved using the learned dictionary to realize target configuration recognition. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) database demonstrate that the proposed algorithm is capable of handling the situations with limited number of training samples and under noise conditions.