针对稀疏表示高光谱检测算法性能受背景字典影响较大的问题,充分利用高光谱图像空间信息和光谱主成分信息,提出了一种基于字典学习的稀疏表示异常检测算法。首先利用主成分分析提取高光谱数据的主特征,建立目标主成分空间,并证明了在主成分空间进行字典学习稀疏重构的可行性；然后在主成分空间内构造基于K-SVD算法的训练字典,改善了背景字典性能；采用正交匹配算法重构主成分分量,利用主成分分析反变换得到待检测像元重构光谱,增强了高光谱图像的局部异常特性；最后,基于重构误差异常特性实现高光谱图像异常检测。仿真结果证明了该方法的有效性。

The performance of algorithm is seriously affected by background dictionary in sparse representation based hyperspectral detection. In this paper, a new dictionary learning (DL) sparse representation (SR) based algorithm for anomaly detection in hyperspectral imagery (HSI) was proposed. Principal component analysis (PCA) was used to extract main character of hyperspectral data. Thus, principal component space was established and the feasibility of DLSR in principal component space was proved. Then, the K-SVD algorithm based background dictionary was locally generated using dual window centered at the pixel of interest. Orthogonal matching pursuit (OMP) algorithm and PCA inverse transform were used to reconstruct the original spectrum based on trained dictionary. Thus, regions containing targets were detected by using spectral reconstruction error. Experimental results indicate that the proposed algorithm is effective.