Sparsity constraint nearest subspace classifier for target recognition of SAR images

This paper proposes a sparsity constraint nearest subspace classifier (SNSC) for target recognition of synthetic aperture radar (SAR) images. Unlike optical images, SAR images are highly sensitive to target azimuth. Therefore, the global dictionary collaborated by samples from different classes has high between-class correlation, which will impair the performance of sparse representation-based classification (SRC). Furthermore, even on the subspace spanned by a single class, only a small number of samples with similar azimuths to the test image are highly correlated with the test image. Thus, the linear coefficients over the subspace are actually sparse ones. Therefore, in this paper we impose sparsity constraint on nearest subspace classifier (NSC) classifier and apply it to SAR target recognition. The target label of the test sample is decided to be the class with the minimum reconstruction error. The proposed method is tested on moving and stationary target acquisition and recognition (MSTAR) dataset and compared with several state-of-the-art methods and the experimental results verify the validity and robustness of the proposed method.