一种基于自适应核字典学习的SAR目标识别方法

摘要

提出一种基于自适应核字典学习的合成孔径雷达(synthetic aperture radar,SAR)目标识别方法.该方法首先将SAR图像的特征信息通过核函数映射到高维度的核空间中并进行字典学习;然后根据更新后的字典动态计算稀疏度;最后依据最小重构误差准则实现SAR目标识别.在公开数据集MSTAR上的仿真实验结果表明,该方法提取到的特征信息可分度高,对SAR目标的识别具有较好的性能.%A synthetic aperture radar (SAR) target recognition method based on adaptive kernel dictionary learning is proposed in order to enhance the ability of sparse representation to extract non-linear feature information. Firstly, the SAR image feature information is mapped into a high-dimensional kernel space through a kernel function, and then the dictionary is learned in the high-dimensional kernel space. Next, the sparsity is dynamically calculated according to the information of each dictionary update. Finally, the SAR target recognition is achieved by minimizing the reconstruction error. The simulation results on MSTAR data sets show that the feature information extracted by this method can be highly indexed and has better performance on SAR target recognition.