结合全局和局部稀疏表示的SAR图像目标识别方法

Target recognition of SAR images based on combinationof global and local sparse representations

提出结合全局和局部稀疏表示的合成孔径雷达(SAR)目标识别方法。基于全局字典的稀疏表示可以比较各个训练类别对于测试样本的相对表征能力。而基于局部字典的稀疏表示则体现各个类别对于测试样本的绝对描述能力。因此,两者的结果具有良好的互补性,可以为正确决策提供更充分的信息。采用D-S (Dempster-Shafer)证据理论对两者的决策矢量(即重构误差)进行决策融合从而得到更为稳健的识别结果。基于MSTAR数据集进行了目标识别实验并与其他SAR目标识别方法进行了充分对比,实验结果证明了提出方法的有效性。

This paper proposes a synthetic aperture radar(SAR) target recognition method based on combination of global and local representations. Sparse representation over the global dictionary could effectively compares the relative description capabilities of different classes for the test sample. However, local dictionary-based sparse representation reflects the absolute description ability of each category on the test sample. Therefore, the two representations could complement each other to provide more information for correct decisions. The decision value vectors(i.e., reconstruction errors) from the global and local representations are fused by Dempster-Shafer(D-S) evidence theory for robust target recognition. Experiments are conducted on public moving and stationary target acquisition and recognition(MSTAR) dataset to be compared with other SAR target recognition methods. The experimental results show the effectiveness of the proposed method.