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Classification of high-spatial resolution imagery based on distance-weighted Markov random field with an improved iterated conditional mode method

In this article, we propose a Distance-Weighted Markov Random Field (DwMRF) for classification of high–spatial resolution imagery. The proposed DwMRF integrates the spectral and spatial information of the image, and better coordinates the interaction between neighbours and the central pixels than the conventional Equal-weighted MRF (EwMRF). In addition, we propose a Serial Iterated Conditional Mode (SICM) method for the solution of the Markov Random Field (MRF) model. Experiments are conducted on three data sets: HYDICE data of the Mall in Washington, DC, HYMAP data of Purdue University and QuickBird data of Beijing. We compare the proposed DwMRF approach with other methods: the EwMRF, Maximum Likelihood Classification (MLC) and a multiresolution segmentation (Fractal Net Evolution Approach (FNEA)) method. Experiments show the DwMRF is robust and outperforms the other methods; furthermore, the proposed SICM method converges more rapidly than conventional Iterated Conditional Mode (ICM) and provides classification results comparable with the conventional ICM method.