IMAGE CLASSIFICATION USING SPECTRAL AND SPATIAL INFORMATION-BASED ON MRF MODELS

A new criterion for classifying multispectral remote sensing images or textured images by using spectral and spatial information is proposed . The images are modeled with a hierarchical Markov Random Field (MRF) model that consists of the observed intensity process and the hidden class label process. The class labels are estimated according to the m aximum a posteriori (MAP) criterion, but some reasonable approximation s are used to reduce the computational load. A stepwise classification algorithm is derived and is confirmed by simulation and experimental results.