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Markov random field (MRF) is an effective method for description of local spatial-temporal dependence of image and has beenwidely used in land cover classification and change detection. However, existing studies only use pair-point clique (PPC) to describespatial dependence of neighbouring pixels, which may not fully quantify complex spatial relations, particularly in high spatialresolution images. In this study, multi-point clique (MPC) is adopted in MRF model to quantitatively express spatial dependenceamong pixels. A modified least squares fit (LSF) method based on robust estimation is proposed to calculate potential parameters forMRF models with different types. The proposed MPC-MRF method is evaluated and quantitatively compared with traditional PPCMRFin urban land cover classification using high resolution hyperspectral HYDICE data of Washington DC. The experimentalresults revealed that the proposed MPC-MRF method outperformed the traditional PPC-MRF method in terms of classificationdetails. The MPC-MRF provides a sophisticated way of describing complex spatial dependence for relevant applications

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

A COMPARATIVE STUDY BETWEEN PAIR-POINT CLIQUE AND MULTI-POINT CLIQUE MARKOV RANDOM FIELD MODELS FOR LAND COVER CLASSIFICATION