SAR target classification using multi-aspect multi-feature collaborative representation

Abstract

ABSTRACT In this paper, a novel approach is proposed for Synthetic Aperture Radar (SAR) target classification based on multi-aspect multi-feature collaborative representation. Firstly, principal component analysis (PCA), wavelet and 2-dimensional slice Zernike moments (2DSZM) features are extracted from SAR images. Next, based on the strong correlation among the adjacent aspect SAR target images, we extend the basic collaborative representation classification (CRC) model to a neighbourhood multi-aspect CRC model. For each feature of the current test sample, neighbourhood multi-aspect test samples are regarded as the input to the model, then the temporary label is obtained for the current test sample under this feature. Finally, the temporary label is fused using the voting method to get the final classification result. The novelty of the proposed method is to improve the performance of target classification by integrating representation learning ability of different features and exploiting neighbourhood multi-aspect correlation. Experiments are investigated on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The results show that the proposed algorithm can achieve a 98.52% overall accuracy and is superior to state-of-the-art methods for SAR target classification.