Generally

the analyzed results from remote sensing data are uncertain and multi solution

which is determined by the characteristics of global surface information being multi dimensional and infinite. Therefore

remote sensing information has some degree of definite statistical characteristic

but as well as holds the high randomness and complexity

which generally behaves as mixture density distribution in feature space. In allusion to the complexity of statistical distribution of remote sensing information

in this study we firstly introduce into the finite mixture model and its expectation maximization(EM) algorithm for decomposing the mixture distribution into finite parametric density distributions in order to simulate or approach the whole mixture distribution. By the model it should be firstly assumed that whole distribution could be separated into infinite parametric density distributions

then by EM iterative computation the maximum likelihood parameters of each proportional distribution can be estimated. Furthermore

the finite mixture model and its EM algorithm are extended to clustering algorithm for remotely sensed data. By the experimental case

the EM clustering algorithm is synthetically compared with conventional statistical clustering algorithm. The results show that the EM algorithm has several particular advantages such as self adaptive decision for clustering number

extensibility of prior knowledge integration and free initialization

etc.