Flood Disaster Detection with Dual-Polarization SAR Data Considering the Impact of Rainfall

With the development of the polarimetric statistical measures, change detection algorithms have been widely applied in the flood disaster detection. The representative methods utilize the Wishart distance to generate the difference map between the pre- and the post-disaster data. Furthermore, the difference map is applied into the threshold segmentation to generate the binary result. However, the existing methods rarely consider the impact of rainfall on the polarimetric distance, which causes abnormal polarimetric differences and incorrect detection results. Based on the fact of different sensitivities of the co- and the cross-polarization intensity for the rainfall, this paper uses the cross ratio of two polarization intensities to eliminate the impacts of the rainfall events on the polarimetric distances. Then the Markov Random Field (MRF) model is utilized to detect the flood disaster with the initialization of OTSU threshold segmentation. Two Sentinel-1 SAR data in the Poyang Lake Basin is used to assess the effectiveness of our method. The results have demonstrated the superiority of our method over the conventional methods, especially in the rainy areas.