由于K—means聚类要求每个像素要和所有聚类中心求欧氏距离，因此当聚类数很多时，这是一个相当耗时的工作。改进后的K—means聚类算法使类内像素只通过和相邻的聚类中心进行距离计算来聚类，由于随着算法的迭代进行，大量类的状态基本固定，因此使得聚类速度不断加快。多层次聚类无损压缩就是利用改进的K—means聚类算法具有快速收敛的特点，和利用分层次去冗余的方法来聚类，因此可最大限度消除残差冗余。基于SP整数小波变换的多层次聚类由于其不仅能消除空间冗余、结构冗余，还能进一步对残差数据去冗余，因而实现了多光谱遥感图象无损压缩的突破。最后通过不同算法对TM图象进行压缩的比较和参数分析，论证了多层次聚类无损压缩的高效及合理性。

Every pixel in the super space is required by K means algorithm to calculate Euclidean distance for clustering. When there are much many class centers, this is a much hard work. In this paper, an improved K means clustering algorithm is presented to accelerate clustering process with more and more classes becoming stable by judging with neighbor centers nearest to the pixel. The inter spectral redundancy and intra spectral redundancy can be eliminated mostly by multilevel clustering algorithm with quickly convergent K means classification and the method clearing redundancy at step through enhancing the intra class pixel redundancy. The multi level clustering process with initial S+P (Sequential transform + Prediction) integer wavelet transformation can not only remove the spatial and structural redundancy, but also delete the residual data redundancy realizing the breakthrough of lossless compression for multi spectral images. Furthermore, the comparison with other lossless compression algorithm and the parameter analysis of the TM (Landsat Thematic Mapper) images show that this multilevel clustering compression algorithm is more reasonable and efficient.