应用局部约束二维稀疏表示识别SAR图像目标

摘要

By analyzing the limitation of the traditional sparse representation based classification,a novel classification framework called two dimensional Local-constrained Coding and Sparse Representation (2D-LSRC)is proposed for Synthetic Aperture Radar (SAR)images recognition. Different from other recent popular vector-based representation,2D-LSRC preserves the global linear coding coefficients between the input matrix and these elementary matrices,as well as the local data structure.Extensive experimental results of MSTAR datasets show the effectiveness of the proposed algorithms and its robustness for the number of the training dataset.%在分析典型稀疏表示分类方法和局限性的基础上，提出了一种基于局部约束的二维稀疏表示方法，以有效解决SAR图像目标识别问题。该方法对SAR图像进行图像预处理，在兼顾图像相邻列（行）对应稀疏表示系数邻近性和样本间局部性的基础上，构建了局部约束目标函数，并通过解闭式解，实现稀疏表示系数的更新求解。利用美国实测MSTAR数据对算法进行了仿真验证，实验结果表明所提出的方法可实现SAR图像目标的有效识别，并对训练样本数目具有一定的鲁棒性。