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.

本文提出了一种基于多角度多特征协同表示的合成孔径雷达（SAR）目标分类的新方法。首先，从SAR图像中提取主成分分析（PCA），小波和二维切片泽尼克矩（2DSZM）特征。接下来，基于相邻方面的SAR目标图像之间的强相关性，我们将基本的协作表示分类（CRC）模型扩展到邻域多方面CRC模型。对于当前测试样本的每个特征，将邻域多方面测试样本视为模型的输入，然后在该特征下获得当前测试样本的临时标签。最后，使用投票方法融合临时标签以获得最终分类结果。该方法的新颖之处在于通过整合不同特征的表示学习能力并利用邻域多方面的相关性来提高目标分类的性能。对移动和固定目标获取与识别（MSTAR）数据集进行了实验研究。结果表明，所提出的算法可以达到98.52％的总体精度，并且优于SAR目标分类的最新方法。