To improve synthetic aperture radar (SAR) target classification performance, both the feature extraction and classification algorithms are considered. The nonsubsampled contourlet transform (NSCT) is employed to decompose SAR images to generate multi-layer components, which provide more discriminative descriptions for the targets. During the classification, the two-stage sparse representations are developed. Each NSCT component is classified using the sparse representation-based classification (SRC). Afterwards, the joint sparse representation (JSR) is adopted to represent the selected NSCT components from the first stage, which are assumed to be highly discriminative. Based on the two decisions, the target label of the test sample is determined. In the experiment, the moving and stationary target acquisition and recognition (MSTAR) dataset is used to set up scenarios for performance evaluation including the standard operating condition (SOC) and several extended operating conditions (EOCs). The results show the superior performance of the proposed methodover some current methods.

中文翻译：

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应用于 SAR 目标分类的 NSCT 特征的两阶段稀疏表示

为了提高合成孔径雷达 (SAR) 目标分类性能，同时考虑了特征提取和分类算法。非下采样Contourlet变换（NSCT）用于分解SAR图像以生成多层分量，为目标提供更具判别性的描述。在分类过程中，开发了两阶段稀疏表示。每个 NSCT 组件都使用基于稀疏表示的分类 (SRC) 进行分类。之后，采用联合稀疏表示（JSR）来表示从第一阶段选择的 NSCT 组件，这些组件被认为是高度区分的。基于这两个决定，确定测试样本的目标标签。在实验中，移动和静止目标采集与识别 (MSTAR) 数据集用于设置性能评估场景，包括标准操作条件 (SOC) 和几个扩展操作条件 (EOC)。结果表明，所提出的方法优于现有的一些方法。