Sparse Representation-Based Inundation Depth Estimation Using SAR Data and Digital Elevation Model

Floods increase every year worldwide, and prompt information about the affected areas is essential for early disaster response. There has been extensive development in applying remote sensing data to identify floods. In fact, remote sensing data are the only tool to identify the extent of large-scale floods within hours after their occurrence. However, few studies have addressed methods to estimate inundation depth. Inundation depth can be advantageous for identifying areas where people may need assistance during evacuation and estimating damage loss. We present a practical application of sparse representation that integrates a synthetic aperture radar-based flood binary map with a digital elevation model to estimate inundation depths. We assume that the floodwaters can be modeled as a combination of water bodies at a state of rest. A dictionary of water bodies computed under potential inundation levels is constructed from the digital elevation model. Then, the actual flood extent is represented as a sparse linear combination of the water body dictionary. The inundation depth can be estimated because each water body from the linear combination is associated with an inundation level. To assess our proposed procedure, we computed the inundation depth of the flood in the town of Mabi, Okayama Prefecture, produced during the 2018 heavy rainfall. An average absolute value difference of about 60 cm between our results and a field survey performed by a third party was observed. Two other floods produced by the 2019 Hagibis typhoon were analyzed to illustrate the relevant information that can provide inundation depths.