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.