针对目前已有的高光谱异常检测算法大多只利用了高光谱图像的光谱维信息，而没有体现高光谱数据“图谱合一”的优势，导致算法检测性能不佳的问题，提出了一种基于非局部自相似性的高光谱异常检测（NLSSAD）算法。首先建立双立体窗，其中内窗表示待测像素光谱向量的空间—光谱三维结构窗，之后在背景中寻找与内窗最为相似的立体窗，并计算二者之间的距离从而得到待测像素光谱向量的非局部自相似性指数，并得到异常检测结果。实验结果表明，与现有的算法相比，所提算法在检测率和运算速度上均有较好的表现。

Most of the existing hyperspectral anomaly detection algorithms only use the spectral dimension information of hyperspectral images, which does not reflect the advantages of “image-spectrum integration” of the hyperspectral data, and may result in degraded detection performance.This paper proposes a hyperspectral anomaly detection algorithm based on non-local self-similarity（NLSSAD）.The algorithm first establishes dual stereoscopic windows, in which the inner window represents the 3D spatial-spectral structure window of the Spectral Vector of the Pixel to be Tested (SVPT).Then the stereoscopic window in the background is found, which is the most similar to the inner window, and the distance between the two windows is calculated to obtain the non-local self-similarity index of SVPT.The anomaly detection results show that, compared with the existing algorithms, NLSSAD has better performance in detection rate and operation speed.