遥感图像分类是遥感图像处理中长期存在的一个难题，针对不同的传感器图像，不同的应用需求，选择合适的分类算法非常重要。在分类中不仅要考虑分类的精度，而且要考虑分类效率。本文研究了K均值算法的初始聚类中心的选择对算法本身聚类精度及效率的影响，提出了一种高效高精度的初始聚类中心选取方案，实验结果表明。利用该算法进行地表分类，效率比ENVI的K-Means（K均值）模块高。

Remote Sensing Image Classifiction is always a difficult problem.It's very important to use proper algorithms for different images.Not only precision but also efficiency must be considered.By researching in the relations between the initial means of clusters and the efficiency of clustering,we proposed a method of computing the initial means of clusters for K-Means Clustering.It is proved to be more efficient than the(K-Means) Clustering module of ENVI when it's used in Remote Sensing Classification.