Abstract: A new hierarchical Markov model in wavelet domain was proposed.In this model,the Gauss Markov random field(GMRF) was used to model the distribution of wavelet coefficient vectors to describe the relationship of observed features on each scale,and the cooperation of interscale casual.Innnerscale non-casual Markov Random Fields was exploited to model the label field priori probability.Based on the Bayesian rules,a new textured image segmentation algorithm was proposed employing multi-objective problem solving technique in this new hierarchical model.Experiments with synthetic texture images and remote sensing images were carried out.The results show the abilities of the proposed method to reduce segmentation error rate.