针对模糊C均值算法对数据分布状态和初始聚类中心过于依赖的问题，利用已知样本信息，提出了一种改进的单点逼近型加权模糊C均值算法。该算法首先通过对原始数据进行概率统计和加入样本属性权值来调整数据为均匀分布；然后采用先验样本单点逼近的方法来消除先验样本选取的影响，从而不仅得到了合适的初始聚类中心，而且有效地加快了算法的收敛速度和提高了聚类的精度；最后将改进后算法与遥感数据特点相结合，构成了完整的遥感图像地物聚类算法。通过UCI数据集和扎龙湿地遥感数据的试验结果的比较证明，该改进方法是真实有效的。

Focusing on the fuzzy C-means algorithm’s problem that the cluster quality is greatly affected by the data distribution and the stochastic initializing the centrals of cluster,a single-point approximation weighted fuzzy C-means algorithm is proposed by using the part of prior samples information.After the probability statistics of original data is conducted,the weights of data attribute are designed to adjust to the uniform distribution,and then are added in the process of cyclic iteration.What’s more,in order to significantly improve the convergence speed and the cluster precision,the proper initial cluster centers are chosen by the single adjustment algorithm,which can also overcome the selection influence of prior samples.In addition,combined with the characteristics of remote sensing data,the modified algorithm is updated for remote sensing image cluster.With the comparison experiment of the UCI data sets and the Zhalong wetland remote sensing data,the real validity of proposed algorithm is proved.