A Bayesian network approach to perform SAR/InSAR data fusion in a flood detection problem

The exploitation of a multi-temporal stack of SAR intensity images seems to provide satisfactory results in flood detection problems when different spectral signature in presence of inundation are observed. Moreover, the use of interferometric coherence information can further help in the discrimination process. Besides the remote sensing data, additional information can be used to improve flood detection. We propose a data fusion approach, based on Bayesian Networks (BNs) , to analyze an inundation event, involving the Bradano river in the Basilicata region, Italy. Time series of COSMO-SkyMed stripmap SAR images are available over the area. The following random variables have been considered in the BN scheme: F, that is a discrete variable, consisting of two states: flood and no flood; the n-dimensional i variable, obtained by the SAR intensity imagery; the m-dimensional γ variable, obtained by the InSAR coherence imagery; the shortest distance d of each pixel from river course. The proposed BN approach allows to independently evaluate the conditional probabilities P ( i | F ), P (γ| F ) and P ( F | d ), and then to join them to infer the value P ( F = flood | i , γ, d ), obtaining the probabilistic flood maps (PFMs). We evaluate these PFMs through comparisons with reference flood maps, obtaining overall accuracies higher than 90%.