不同遥感对象，光谱曲线的突变点位置差异很大，不同尺度的小波变换可有效提取这些突变特征，在此基础上，用小波特征相关系数描述像素的近似程度，取代一般聚类算法以欧氏距离为基础的聚类概念，聚类结果可准确反映遥感对象内容，基于小波特征抽取和模拟退火的多光谱遥感图象快速聚类算法，通过扩展频段，增加特征点的个数以丰富类的特性，对空间数据进行均匀抽样产生聚类空间，采用模拟退火技术和逐步降低聚类规模的方法，快速实现全局最优的聚类中心，类内评价最优代表作为聚类中心，保证类特性的持续性和强壮性，而且解决了K－means聚类的参数选择问题，最后采用TM多光谱遥感图象进行参数分析和算法比较，验证了该算法分类快速准确，且参数控制灵活，因此基于小波特征抽取和模拟退火的多光谱遥感图象快速聚类算法有较好的应用前景。

The differences of the spectral curves among different objects are so obvious and important to be extracted through the wavelet transform at different scale. The traditional clustering concepts based on Euclidean distance are also redefined by the wavelet feature correlation coefficient to accurately describe the content of the remotely sensed objects. The fast clustering algorithm for multispectral images based on wavelet feature and simulated annealing increases the number of characteristic points by expanding the spectral bands to enrich the feature of the classes; clustering space is formed by evenly sampled dots; furthermore, simulated annealing leads to the best class centers on the whole scope at a high speed by decreasing the clustering scale and temperature step by step; the class characters is remained strong and durative by choosing the best dot as the class center; it also resolves the initial parameter problem of K means algorithm. The experimental results of Mississippi Thematic Mapper images show that this clustering algorithm is more efficient than other ordinary clustering algorithms such as K means and ISODATA according to the clustering accuracy and speed. Therefore, there are fairly prosperous applications on multispectral images for this fast clustering based on spectral wavelet features extraction and simulated annealing algorithm.