提出了一种光谱角匹配(SAM)加权核特征空间分离变换(KEST)高光谱异常检测算法.在基于核的特征空间分离变换(KEST)算法基础上，利用光谱角匹配(SAM)测度对高维特征空间中检测点邻域差异相关矩阵(DCOR)中的每个样本引入权重因子，各样本权重因子取决于该样本光谱向量与检测窗口数据中心向量夹角，从而抑制检测窗口中的病态数据，突出主成分数据的贡献，使得DCOR矩阵能够更好地描述目标、背景数据分布差异.通过理论分析和对模拟、实际数据实验比较，证明该算法较传统异常检测算法和KEST算法具有更高的检测率.

A SAM weighted KEST algorithm based on kernel eigenspace separation transform (KEST) was proposed for anomaly detection in hyperspectral imaging. Weights are introduced for each sample in the difference correlation matrix (DCOR), and the input pixel neighbor surroundings. All samples were weighted according to the angle between the sample spectral vector and the centered vector in detection window to minimize the influence of anomalous data and outstand the contribution of principle component. In this way, DCOR represented the difference between target and background distribution much better. Experimental results indicate that the proposed method shows superior performance over the conventional anomaly detection algorithms and KEST.