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The accurate discrimination of distinct thematic classes using classification techniques developed for medium/low resolution images is not effective when apply to very high spatial resolution (HR) data (e.g. Quickbird, IKONOS) due to the spatial heterogeneity issue. In this paper, Markov random field (MRF) models, which are useful tools for integrating contextual (considering spatial dependence within and between pixels) information into classification process is used to model spatial heterogeneity for improving the classification accuracy. Two novel MRF approaches are evaluated using a Quickbird HR image covers Xixi National Wetland Park, Hangzhou, China. The experimental results show this method is effective to exact segmentation of land boundaries and suppress classification noises. In addition, the improved MRF models outperform than conventional method in terms of classification accuracy and time-efficiency.