A stack of Sentinel-1 InSAR data in an urban area where flood events recurrently occur, namely Beletweyne town in Somalia, has been analyzed. From this analysis, a novel method to deal with the problem of flood mapping in urban areas has been derived. The approach assumes the availability of a map of persistent scatterers (PSs) inside the urban settlement and is based on the analysis of the temporal trend of the InSAR coherence and the spatial average of the exponential of the InSAR phase in each PS. Both interferometric products are expected to have high and stable values in the PSs; therefore, anomalous decreases may indicate that floodwater is present in an urban area. The stack of Sentinel-1 data has been divided into two subsets. The first one has been used as a calibration set to identify the PSs and determine, for each PS, reference values of the coherence and the spatial average of the exponential of the interferometric phase under standard non-flooded conditions. The other subset has been used for validation purposes. Flood maps produced by UNOSAT, analyzing very-high-resolution optical images of the floods that occurred in Beletweyne in April–May 2018, October–November 2019, and April–May 2020, have been used as reference data. In particular, the map of the April–May 2018 flood has been used for training purposes together with the subset of Sentinel-1 calibration data, whilst the other two maps have been used to validate the products generated by applying the proposed method. The main product is a binary map of flooded PSs that complements the floodwater map of rural/suburban areas produced by applying a well-consolidated algorithm based on intensity data. In addition, a flood severity map that labels the different districts of Beletweyne, as not, partially, or totally flooded has been generated to consolidate the validation. The results have confirmed the effectiveness of the proposed method.
Enhancement of Underwater Optical Images Based on Background Light Estimation and Improved Adaptive Transmission Fusion
