Multiresolution and Multimodality Sar Data Fusion Based on Markov and Conditional Random Fields for Unsupervised Change Detection

Current satellite missions (e.g., COSMO-SkyMed, Sentinel-1) collect single- or multipolarimetric synthetic aperture radar (SAR) images with multiple spatial resolutions and possibly short revisit times. The availability of heterogeneous data requires effective methods able to exploit all the available information. In the context of environmental monitoring and natural disaster recovery, this paper proposes an unsupervised change detection method able to properly fuse and exploit multiresolution and multimodality SAR data. The data fusion process is based on the estimation of the virtual images that would have been collected in case all the sensors worked at the same spatial resolution and on the definition of a probabilistic model based on generalized Gaussian distributions and Gram-Charlier approximations. The detection of changes is addressed in a probabilistic graphical framework through a novel conditional random field, by defining an energy function that is minimized through graph-cuts or belief propagation methods.