Finite Mixture Model and Its EM Clustering Algorithm for Remote Sensing Data
有限混合密度模型及遥感影像EM聚类算法

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.