Flood Detection Using Empirical Bayesian Networks

Flood mapping from Synthetic Aperture Radar (SAR) data has attracted considerable attention in recent years. Flood is not only one of the widest spread natural disasters, which regularly causes large numbers of casualties with rising economic loss, extensive homelessness and disaster induced disease, but is also the most frequent disaster type. A valuable information source for such a procedure can be remote sensing synthetic aperture radar (SAR) imagery. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground. For this reason, a data fusion approach of remote sensing data with ancillary information can be particularly useful. In this work, an Empirical Bayesian network is proposed to integrate remotely sensed data, such as multitemporal SAR intensity images and interferometric-SAR coherence data, with geomorphic and other ground information where as in the previous work the authors has used the Bayesian networks. The methodology is tested on a case study regarding a flood that occurred in the Visakhapatnam (India) on October 2014, monitored using a time series of TerraSAR-X data. It is shown that the synergetic use of different information layers can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which may affect algorithms based on data from a single source. The produced flood maps are compared to data obtained independently from the analysis of optical images; the comparison indicates that the proposed methodology is able to reliably follow the temporal evolution of the phenomenon, assigning high probability to areas most likely to be flooded, in spite of their heterogeneous temporal SAR/InSAR signatures, reaching accuracies of up to 89%.