SAR Target Configuration Recognition via Multi-Manifold Based Sparse Representation

Abstract

A multi-manifold based sparse representation (MMSR) algorithm which can preserve the local structure of the datasets in the reconstruction space with multiple descriptions is proposed for synthetic aperture radar (SAR) target configuration recognition. Different from the traditional manifold learning-based algorithms that preserve the local structure of the datasets in the low-dimensional space, the proposed MMSR guarantees local structure preserving of the datasets after feature extraction and sparse representation (SR). The proposed MMSR can avoid corrupted relationships of the samples to realize more accurate SAR target configuration recognition. Satisfying results are obtained on the moving and stationary target acquisition and recognition (MSTAR) database.