Markov Random Field (MRF) models have been successfully utilized in many digital image processing problems such as texture modeling and region labeling. Although MRF provides a well-defined statistical approach for the analysis of images, one disadvantage is the expensive computational cost for the processing and sampling of large images, since global features are assumed to be specified through local descriptions. In this study, a methodology is explored that reduces the computational burden and increases the speed of image analysis for large images, especially airborne and space-based remotely sensed data. The Bayesian approach is suggested as a reasonable alternative method in parameter estimation of MRF models; the utilization of a multiresolution framework is also investigated, which provides convenient and efficient structures for the transition between local and global features. The suggested approach is applied to the simulation of spatial pattern using MRF.