Hierarchical Markov random fields for high resolution land cover classification of multisensor and multiresolution image time series

This chapter presents an overview of the major concepts and of the recent literature in the area of remote sensing data fusion. It describes two advanced methods for the joint supervised classification of multimission image time series, including multisensor optical and Synthetic Aperture Radar (SAR) components acquired at multiple spatial resolutions. The two techniques address different problems of supervised classification of satellite image time series and share a common methodological formulation based on hierarchical Markov random field models. The use of multisensor data in image classification has become increasingly popular with the increased availability of sophisticated software and hardware facilities to handle the increasing volumes of data. A major property of the proposed hierarchical Markovian framework is its flexibility. The graphical architecture associated with multiple quad-trees in cascade allows the incorporation of input image sources associated with different sensors, acquisition times and spatial resolutions - jointly.