A novel method for potential calculation in Markov random field by incorporating spatial dependence in spectral feature

Markov random field (MRF) is one of most powerful tools for description of spatial information. Utilizing spatial information characterized by MRF, classification of remote sensing image becomes more reliable and accurate. At the same time, however, MRF also results in the loss of image details such as fine scaled objects and the wrong boundaries. For this reason, we propose a potential calculation method in which spatial dependence in spectral feature is taken into consideration. To assess its performance, HYDICE of Washington is utilized. Results exhibits, compared with traditional MRF, the proposed one achieves better performance both in accuracy assessment and visual inspection.