A synthetic aperture radar (SAR) target classification method has been developed, in the study, based on dynamic target reconstruction. According to SAR azimuthal sensitivity, the truly useful training samples for the reconstructing the test sample are those with approaching azimuths and same labels. Hence, the proposed method performs linear presentation of the test sample on the local dictionary established by several training samples selected from each class under the azimuthal correlation. By properly adjusting the azimuthal correlation constraint, the test sample can be reconstructed at different levels by different scales of training samples. During the classification phase, the reconstruction error vectors from different levels are combined by linear fusion and the label of the test sample is determined based on the fused errors. Experimental conditions are setup on the moving and stationary target acquisition and recognition (MSTAR) dataset to evaluate the proposed method. The results confirm the effectiveness of the proposed method.

中文翻译：

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基于方位约束重构的SAR图像目标识别

在研究中，基于动态目标重建，已经开发了一种合成孔径雷达（SAR）目标分类方法。根据SAR方位角敏感性，用于重建测试样本的真正有用的训练样本是那些具有接近方位角和相同标签的训练样本。因此，所提出的方法在由方位角相关性下从每个类别中选择的几个训练样本建立的本地字典上执行测试样本的线性表示。通过适当地调整方位角相关约束，可以通过不同比例的训练样本在不同级别上重构测试样本。在分类阶段，通过线性融合将不同级别的重构误差向量进行组合，并根据融合误差确定测试样品的标记。在移动和固定目标获取与识别（MSTAR）数据集上设置实验条件，以评估所提出的方法。结果证实了所提方法的有效性。