A HMGMRF Model and Its Application in High-resolution Imagery Segmentation
HMGMRF模型及其在高分辨率影像分割中的应用

In this paper a Hierarchical Multispectral Gauss Markov Random Field (HMGMRF) model and its corresponding segmentation algorithm are proposed by modifying approach of anticipation dispersion of Gauss Markov Random Field (GMRF).In the segmentation procedure,the HMGMRF model is first used to analyze variational tendency of each land-cover classes in multispectral bands (i.e.multispectral texture characters of land-cover classes),neighborhood space is extended from single layer to multi-layer by introducing correlations of the spectral bands of remote sensing imagery,dimension of texture character is extended,thus capability to describe texture characters of the model is improved.Then,based on Bayesian principle,Expectation Maximization algorithm is accompanied by the estimation of model parameter on each land-cover classes.Finally,based on intensity texture characters,Maximum a posteriori is employed to perform image segmentation.Experimental results show that the proposed HMGMRF model-based segmentation algorithm is more capable in differentiating land cover classes and thus can achieve higher segmentation accuracy.