A method based on tree-structured Markov random field and a texture energy function for classification of remote sensing images

A method for classification based on tree-structured Markov random field (TS-MRF) and a texture energy function (TEF) is presented. The TEF consists of a second-order prior energy function with homogeneous-non internal and external fields obtained from 2-D Wold decomposition. Thus, in TEF is possible to characterize a combination of stochastic and structural texture. The TEF is used into binary MRFpsilas defined in the TS-MRF model. The extended model TS-MRF/TEF is tested on remote sensing images for mangrove cover classification. An evaluation is carried out comparing results with a thematic map of study area. The classification results using TS-MRF/TEF are compared with results produced using supervised and unsupervised pixel-based classification methods. We show that incorporating the TEF into TS-MRF model the quality of the mangrove classification is improved.