ABSTRACT

A multi-criteria sparse representation-based classification (SRC) is developed for synthetic aperture radar (SAR) target recognition. The sparse representation is first performed on the testing sample to obtain the coefficient vector over the global dictionary. Accordingly, three different decision criteria are employed for classification, i.e., the minimum global reconstruction error, the minimum local reconstruction error, and the maximum coefficient energy. The three rules exploit the coefficient vector from different aspects so they can complement each other. A linear fusion is conducted on the three result with an adaptive weighting algorithm and a final decision is reached. Based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, the proposed method is investigated under the standard operating condition (SOC), noise corruption, and occlusion. The results confirm its validity and robustness.

摘要

为合成孔径雷达 (SAR) 目标识别开发了一种基于多准则稀疏表示的分类 (SRC)。首先对测试样本进行稀疏表示，以获得全局字典上的系数向量。因此，采用三种不同的决策标准进行分类，即最小全局重构误差、最小局部重构误差和最大系数能量。这三个规则从不同方面利用系数向量，因此它们可以相互补充。使用自适应加权算法对三个结果进行线性融合，并得出最终决定。基于移动和静止目标采集与识别（MSTAR）数据集，在标准操作条件（SOC）下研究了所提出的方法，噪声损坏和遮挡。结果证实了其有效性和稳健性。