结合多决策准则稀疏表示的SAR图像目标识别方法

提出组合多决策准则的稀疏表示分类（Sparse Representation-based Classification，SRC）并在合成孔径雷达（Synthetic Aperture Radar，SAR）目标识别中进行应用。传统SRC通常在全局字典上对测试样本进行重构，分别计算不同训练类别对于测试样本的重构误差，最终根据最小重构误差的原则进行分类决策。然而，由于SAR目标识别问题的复杂性，单一决策准则往往对扩展操作条件的适应性不强，导致整体性能下降。为此，文中基于稀疏表示求解的系数矢量，分别采用最小重构误差原则、最大系数能量原则以及局部最小重构误差原则分别进行分类。最小重构误差准则直接采用传统算法。最大系数能量准则分别计算不同训练类别系数能量，按照能量最大的原则进行判决。局部最小重构误差原则在局部字典上对测试样本进行表征和分析，充分体现SAR图像的视角敏感性。对于三个准则获取的决策变量，通过适当转换统一采用概率分布形式进行表达。最终，基于线性加权融合对三个准则的结果进行分析，判决测试样本所属目标类别。基于MSTAR数据集对方法进行测试，分别验证了提出方法在标准操作条件、俯仰角差异、噪声干扰及目标遮挡等情形的性能。实验结果表明:所提方法通过结合多决策准则能够有效提升SAR目标识别性能。

Combination of multiple decision principles based on sparse representation-based classification for target recognition of SAR image

A synthetic aperture radar (SAR) target recognition method using multiple decision principles in sparse representation-based classification (SRC) was proposed. The traditional SRC generally reconstructed the test sample on the global dictionary and calculated the reconstruction errors of individual training classes. And the decision was reached based on the minimum reconstruction error. However, because of the complexity of SAR target recognition, a single decision principle probably had low adaptivity to the extended operating conditions (EOC). Therefore, this paper employed the global minimum reconstruction, maximum coefficient energy, and local minimum reconstruction error principles to make decisions based on the solved coefficient vector from sparse representation. The global minimum reconstruction error principle directly adopted the traditional one. The maximum coefficient energy principle calculated the coefficient energies of different classes and made decision based on the maximum one. The local minimum reconstruction error principle represented and analyzed the test sample on the local dictionary so the azimuthal sensitivity of SAR imaging could be exploited. For the decision values from the three principles, they were transformed to the same type of probability vectors. Finally, the linear fusion was performed to combine their decisions. Experiments were conducted on the MSTAR dataset under situations including the standard operating condition (SOC), depression angle variance, noise corruption, and target occlusion. The results validate that the combination of multiple decision principles could effectively improve SAR target recognition performance.