A multiview synthetic aperture radar (SAR) target recognition with discrimination and correlation analysis is proposed in this study. The multiple views are first prescreened by a support vector machine (SVM) to select out those highly discriminative ones. These views are then clustered into several view sets, in which images share high correlations. The joint sparse representation (JSR) is adopted to classify SAR images in each view set, and all the decisions from different view sets are fused using a linear weighting strategy. The proposed method makes more sufficient analysis of the multiview SAR images so the recognition performance can be effectively enhanced. To test the proposed method, experiments are set up based on the moving and stationary target acquisition and recognition (MSTAR) dataset. The results show that the proposed method could achieve superior performance under different situations over some compared methods.

中文翻译：

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多视图SAR图像的判别和相关分析及其在目标识别中的应用

提出了一种具有识别和相关分析的多视场合成孔径雷达（SAR）目标识别方法。多个视图首先由支持向量机（SVM）进行预筛选，以选择那些具有高度区分性的视图。然后，将这些视图聚集成几个视图集，其中图像共享高度相关性。采用联合稀疏表示（JSR）对每个视图集中的SAR图像进行分类，并使用线性加权策略融合来自不同视图集的所有决策。该方法对多视点SAR图像进行了更加充分的分析，可以有效地提高识别性能。为了测试该方法，基于运动和静止目标获取与识别（MSTAR）数据集进行了实验。