Joint classification of panchromatic and multispectral images by multiresolution fusion through Markov random fields and graph cuts

Abstract

The problem of the supervised classification of multiresolution images, composed of a higher-resolution panchromatic channel and of several coarser-resolution multispectral channels, is addressed in this paper by proposing a novel contextual method based on Markov random fields. The method iteratively exploits a linear mixture model for the relationships between data at different resolutions and a graph-cut approach to Markovian energy minimization to generate a contextual classification map at the highest resolution available in the input data set. The estimation of the parameters of the method is carried out by extending recently proposed techniques based on the expectation-maximization and Ho-Kashyap's algorithms. The method is experimentally validated with semisimulated and real data involving both IKONOS and Landsat-7 ETM+ images and the results are compared with those generated by a previous Bayesian multiresolution classification technique.