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.