Considering the defaults in synthetic aperture radar (SAR) image feature extraction, an SAR target recognition method based on non-subsampled Shearlet transform (NSST) was proposed with application to target recognition. NSST was used to decompose an SAR image into multilevel representations. These representations were translation-invariant, and they could well reflect the dominant and detailed properties of the target. During the machine learning classification stage, the joint sparse representation was employed to jointly represent the multilevel 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.

中文翻译：

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基于非二次采样的Shearlet变换（NSST）的特征提取及其在SAR图像数据中的应用

针对合成孔径雷达（SAR）图像特征提取中的默认问题，提出了一种基于非下采样Shearlet变换（NSST）的SAR目标识别方法，并将其应用于目标识别。NSST用于将SAR图像分解为多级表示。这些表示是翻译不变的，它们可以很好地反映目标的主导和详细特性。在机器学习分类阶段，采用联合稀疏表示来共同表示多级表示。联合稀疏表示可以独立考虑各个组件，同时考虑不同组件之间的内部相关性。因此，可以提高联合表示的精度。最后，根据总重建误差确定测试样品的目标标签。在MSTAR数据集上进行了实验以检验该方法，结果证实了该方法在标准操作条件，构型方差，下俯角方差和噪声破坏下的有效性和鲁棒性。