Semi-automated estimation of the local flood depth on SAR images

Abstract

In the context of a flooding, a clear cloud-free SAR (Synthetic Aperture Radar) image proves mainly useful to retrieve flood features that can provide an extensive understanding of the disaster. Among these features, extremely important is the water depth on which this paper will focus by looking for a semi-automated algorithm for its estimation in the neighborhood of a given building from a pair of SAR images.In this study, two SAR images acquired during dry and flooded conditions are necessary, as well as a DSM (Digital Surface Model) to give an a priori knowledge of the height of the building and its footprint. The whole process is divided into two main parts: First, an extraction of the building's double-bounce contribution using Genetic Algorithms, then the computation of the inundated building's height, to eventually evaluate the water level locally in the neighborhood of this building.Thanks to the semi-automation of the double-reflection line retrieval, the execution time of the whole process was reduced from a few minutes (time to manually delineate the double-bounce line) to a few seconds, while keeping an error in the estimated flood depth in the order of a few decimeters (35cm on average).

A Web Application for the Automatic Mapping of the Flood Extent on Sar Images

“…Among future improvements, one preprocessing step required to precede the classification is the generation of a mask of shadows from the DEM, which represents areas where the flood cannot be detected on the SAR image, because the presence of water there does not affect the backscatter [12]. Also as a future work, the algorithm developed in [13] to give an Table 3. The commission and omission errors for the flood and non-flood classes and the overall accuracy of the classification with the SGD classifier on Tewkesbury 2007 dataset [11].…”