Locality-Constrained and Class-Specific Sparse Representation for Sar Target Recognition

Abstract

Recently, sparse representation has achieved the impressive performance on target recognition in synthetic aperture radar (SAR) image. However, the unstable and unsupervised optimization of the sparse representation may lead to undesired recognition result. In this paper, a locality-constrained and class-specific sparse representation (LCSR) framework is presented to alleviate these problems. Instead of the sparse constraint, the locality constraint is designed to utilize the local structure information of the training samples. It provides stable representation for the samples with minor variations, which is beneficial to classification. To further improve the recognition performance, the query sample is represented as a linear combination of class-specific galleries based on the supervision of class information. The inference is reached corresponding to the class with the minimum reconstruction error. The experimental results demonstrate the effectiveness and robustness of the proposed method.