Hainan Key Laboratory of Earth Observation, Sanya 572029, China.
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China.
Sensors (Basel). 2018 Sep 10;18(9):3019. doi: 10.3390/s18093019.
A target recognition method of synthetic aperture radar (SAR) images is proposed via matching attributed scattering centers (ASCs) to binary target regions. The ASCs extracted from the test image are predicted as binary regions. In detail, each ASC is first transformed to the image domain based on the ASC model. Afterwards, the resulting image is converted to a binary region segmented by a global threshold. All the predicted binary regions of individual ASCs from the test sample are mapped to the binary target regions of the corresponding templates. Then, the matched regions are evaluated by three scores which are combined as a similarity measure via the score-level fusion. In the classification stage, the target label of the test sample is determined according to the fused similarities. The proposed region matching method avoids the conventional ASC matching problem, which involves the assignment of ASC sets. In addition, the predicted regions are more robust than the point features. The Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is used for performance evaluation in the experiments. According to the experimental results, the method in this study outperforms some traditional methods reported in the literature under several different operating conditions. Under the standard operating condition (SOC), the proposed method achieves very good performance, with an average recognition rate of 98.34%, which is higher than the traditional methods. Moreover, the robustness of the proposed method is also superior to the traditional methods under different extended operating conditions (EOCs), including configuration variants, large depression angle variation, noise contamination, and partial occlusion.
提出了一种基于匹配属性散射中心（ASC）到二值目标区域的合成孔径雷达（SAR）图像目标识别方法。从测试图像中提取的 ASC 被预测为二值区域。具体来说，首先基于 ASC 模型将每个 ASC 转换到图像域。然后，将得到的图像转换为通过全局阈值分割的二值区域。从测试样本的各个 ASC 预测的所有二值区域都映射到相应模板的二值目标区域。然后，通过三个得分评估匹配的区域，通过得分级融合将它们组合为相似性度量。在分类阶段，根据融合的相似性确定测试样本的目标标签。所提出的区域匹配方法避免了传统 ASC 匹配问题，其中涉及 ASC 集的分配。此外，预测的区域比点特征更稳健。实验使用 Moving and Stationary Target Acquisition and Recognition（MSTAR）数据集进行性能评估。根据实验结果，在几种不同的工作条件下，本研究中的方法优于文献中报道的一些传统方法。在标准工作条件（SOC）下，该方法表现出非常好的性能，平均识别率为 98.34%，高于传统方法。此外，在不同的扩展工作条件（EOC）下，包括配置变体、大俯角变化、噪声污染和部分遮挡，该方法的稳健性也优于传统方法。
