基于非下采样剪切波特征提取的SAR图像目标识别方法  

SAR Target Recognition Based on Non-subsampled Shearlet Transform (NSST) Feature Extraction

作　　者：丁慧洁 DING Huijie(School of Artificial Intelligence,The Open University of Guangdong,Guangzhou 510091,China)

机构地区：[1]广东开放大学人工智能学院,广东广州510091

出　　处：《探测与控制学报》2020年第1期75-80,共6页Journal of Detection & Control

基　　金：国家开放大学分部2018年度科研课题(G18E2802Z)。

摘　　要：针对现有合成孔径雷达(SAR)图像特征提取方面的不足,提出基于非下采样剪切波(NSST)特征提取的SAR目标识别方法。该方法采用NSST对SAR图像进行分解获得多层次的子代图像,这些子代图像具有良好的平移不变性并且可以很好地反映目标的主要和细节特征。在分类阶段,采用联合稀疏表示对多层次NSST子代图像进行联合表征;联合稀疏表示在独立表示各个分量的同时考察了不同分量之间的相关性,因此可以有效提高联合表征的精度;最终,根据整体重构误差判定测试样本的目标类别。基于MSTAR数据集对提出方法进行测试,实验结果分析表明该方法在标准操作条件、型号差异、俯仰角差异以及噪声干扰的条件下均可以保持优异性能。Considering the defaults in synthetic aperture radar(SAR) image feature extraction, a SAR target recognition method based on non-subsampled Shearlet transform(NSST) was proposed with application to target recognition. NSST was used to decompose a SAR image into multi-level representations. These representations were translation-invariant and they could well reflect the dominant and detailed properties of the target. During the classification stage, the joint sparse representation was employed to jointly represent the multi-level representations. The joint sparse representation could represent individual components independently while considering the inner correlations between different components. Therefore, the precision of joint representation could be enhanced. Finally, the target label of the test sample was determined according to the overall reconstruction error. Experiments were conducted on the MSTAR dataset to examine the proposed method, and the results confirmed its validity and robustness under the standard operating condition, configuration variance, depression angle variance, and noise corruption.

关 键 词：合成孔径雷达 目标识别 非下采样剪切波 联合稀疏表示 MSTAR数据集 

分 类 号：TN957[电子电信—信号与信息处理]