Image classification using spectral and spatial information based on MRF models

Authors:
T. Yamazaki;D. Gingras
Affiliations:
Commun. Res. Lab., Kansai Adv. Res. Center, Kobe;-
Venue:
IEEE Transactions on Image Processing
Year:
1995

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Abstract

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 maximum a posteriori (MAP) criterion, but some reasonable approximations are used to reduce the computational load. A stepwise classification algorithm is derived and is confirmed by simulation and experimental results