高斯混合密降解模型是一种基于稳健统计理论的层次结构的聚类模。GMDD首先假设特征空间是由一组混合的高斯分布组成，然后通过一定的优化算法来获得特征空间中与预称假设相符合的特征分布宵步分离轩到特征空间全部降解为一组混合特征模式的分布集。

Gaussian Mixture Density Modelling and Decomposition (GMDD) is a hierarchical clustering method based on robust statistical theory. Firstly, GMDD is assumed with a mixture group of Gaussian distribution in feature space, then by optimization algorithm the feature which mostly accord with the assumed distribution is hierarchically extracted from space until all of the features in the space are decomposed to a group of featuring pattern. Compared with conventional statistical clustering methods, GMDD's main outstanding superorities are:(1) Initial number of features does not needed to be specified a priori; (2) The proportion of noisy data in the mixture can be large; (3) The parameters estimation of each feature is virtually initial independent; and (4) The variability in the shape and size of the feature densities in the mixture is taken into account. The article presents the model named the GMDD based remote sensing image feature estimation model (GIFEM) , and the model of GA space searching optimization is also presented out.