A synthetic aperture radar (SAR) target recognition method is proposed using monogenic components as basic features. The monogenic signal is employed to decompose original SAR images into multi-scale components. Considering the redundancy and possible indiscrimination in the monogenic components, the nonlinear correlation information entropy (NCIE) is adopted as the criteria for the selection of valid components. The subset of monogenic components with the highest NCIE is chosen and classified by joint sparse representation (JSR). Using the inner correlations of the selected components, JSR could improve the overall reconstruction precision thus enhancing the recognition performance. Experiments are proceeded on the moving and stationary target acquisition and recognition dataset under the standard operating condition and several extended operating conditions, including configuration variances, depression angle variances, noise corruption, and partial occlusion. The results validate the superior effectiveness and robustness of the proposed method over several existed SAR target recognition methods.

中文翻译：

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基于非线性相关信息熵的选定单基因成分联合分类的合成孔径雷达目标识别

提出了一种以单组分为基本特征的合成孔径雷达（SAR）目标识别方法。单基因信号用于将原始SAR图像分解为多尺度分量。考虑到单基因成分的冗余和可能的歧义，采用非线性相关信息熵（NCIE）作为选择有效成分的标准。选择NCIE最高的单基因成分子集，并通过联合稀疏表示（JSR）进行分类。利用所选组件的内部相关性，JSR可以提高整体重建精度，从而提高识别性能。在标准操作条件和几种扩展的操作条件下，对移动和固定目标的采集和识别数据集进行了实验，包括配置差异，下俯角度差异，噪声破坏和部分遮挡。结果证明了该方法在几种SAR目标识别方法上的优越性和鲁棒性。