Abstract. A synthetic aperture radar (SAR) target classification method is proposed by selecting deep features learned by convolutional neural network (CNN) using the nonlinear correlation information entropy (NCIE). The deep feature maps are learned from original SAR images by CNN. Considering the possible indiscrimination and redundancy, all the deep feature maps from different layers are analyzed by NCIE to select out those discriminative ones. The selected feature maps share high inner correlations, which are jointly classified based on joint sparse representation (JSR). Finally, the target label is determined based on the reconstruction errors from JSR. The famous moving and stationary target acquisition and recognition dataset are employed to setup the standard operating condition and several extended operating conditions, including configuration variants, depression angle differences, noise corruption, and partial occlusion, to comprehensively evaluate the performance of the proposed method. The results confirm the superior effectiveness and robustness of the proposed method in comparison with some state-of-the-art SAR target classification methods.

中文翻译：

---

基于非线性相关信息熵的SAR图像目标分类

摘要。利用非线性相关信息熵（NCIE）选择卷积神经网络（CNN）学习到的深层特征，提出了一种合成孔径雷达（SAR）目标分类方法。深度特征图是由 CNN 从原始 SAR 图像中学习的。考虑到可能的不加区分和冗余，NCIE 分析来自不同层的所有深层特征图，以选择那些具有区分性的特征图。选定的特征图共享高内部相关性，这些特征图基于联合稀疏表示（JSR）进行联合分类。最后，根据 JSR 的重构误差确定目标标签。采用著名的运动和静止目标采集识别数据集来设置标准操作条件和几个扩展操作条件，包括配置变体、俯角差异、噪声破坏和部分遮挡，以综合评估所提出方法的性能。与一些最先进的 SAR 目标分类方法相比，结果证实了所提出方法的优越有效性和鲁棒性。