针对高光谱图像异常目标检测中图像邻近像元光谱相似易对检测结果产生干扰的现象，将聚类算法引入到异常目标检测领域，提出一种采用主成分量化和密度估计期望最大聚类的高光谱图像异常目标检测算法.在高维空间中，使用期望最大聚类算法对像元光谱向量进行聚类，将邻近像元的空间相关性转化为类内或类间像元的关系，根据异常像元分布在类别边缘的原理，以类为单位检测异常目标，有效地避免异常点的信息被淹没；另外，针对期望最大聚类算法对初始化过程要求敏感的问题，提出了根据图像的第一主成分信息，分别利用向量量化和密度估计的方法对期望最大聚类算法进行初始化，进一步提高算法的检测效果和计算效率.用合成和真实的AVIRIS高光谱数据进行仿真实验，仿真结果表明使用基于主成分量化和密度估计期望最大聚类算法的高光谱图像异常目标检测算法明显优于传统的异常检测算法.

In order to overcome the problem caused by similar spectrum of the adjacent pixels, the clustering algorithms were introduced into the hyperspectral imagery abnormal target detection. A new algorithm using principal component quantization and density estimation on EM clustering was proposed in this paper. Using EM algorithm to cluster hyperspectral spectrum vectors in the high dimension space, the relations between adjacent pixels in spatial space were be represented by the relations inside or between classes. According to the theory that the abnormal target pixels would spread around the edge of the classes, abnormal target was detected in the unit of class to effectively avoid abnormal point information flooded. And this algorithm achieved good detection effect. For the requirement of EM algorithm initialization is sensitive, in related with the first principal component information of the imagery dataset, EM clustering algorithm was initialized by vector quantization and density estimation method. This can reduce the problems caused by initialization of EM clustering algorithm, and improve the detection effect of the algorithm and computation efficiency. With simulated and real AVIRIS hyperspectral dataset used in simulation experiment, the results show that the proposed anomaly detection algorithm is obviously superior to the traditional detection algorithm.