SAR-based flood detection in urban areas
University of New Brunswick
In Canada, flooding causes more damage to buildings, infrastructures, and people than any other natural disaster. Since floods enormously impact large groups of people, immediate large-scale monitoring is crucial during and after a flood event. In that regard, remote sensing technologies including optical and SAR sensors have been widely used for flood mapping. Nevertheless, SAR sensors have superiority due to their intrinsic day/night and all-weather image acquisition capabilities. While SAR backscatter intensity and phase correlation have been successfully employed in flood detection, other SAR products such as PolSAR decompositions and InSAR phase have been neglected in flood mapping. Moreover, the majority of existing studies are dedicated to flood mapping in rural areas while flood detection in urban areas using SAR imagery has not attracted enough attention. This is mainly due to the complexity of urban structures that pose challenges to the interpretation of backscatter patterns and flood mapping in urban areas. In this study, we examine the synergistic use of PolInSAR features in the improvement of flood mapping in urban environments. Also, the effectiveness of including SAR simulated reflectivity maps that represent the geometric distortion in the SAR image of constructed objects is investigated. Two supervised Machine Learning (ML) models are proposed that employ PolInSAR features along with five auxiliary features namely - elevation, slope, aspect, distance from the river, and land use/land cover- which are well-known to improve flood mapping algorithms. These auxiliary features are considered as the baseline model since they have shown effectiveness in the flood mapping literature. The first ML model is tested on medium-resolution Sentinel1-A images while the second model employs high-resolution TerraSAR-X images along with SAR simulated reflectivity maps. The results show promising improvements with respect to the baseline model: 5.6% overall accuracy improvement in the first ML model, and 9.6% in the second one. This improvement can be interpreted as the successful employment of the selected features and the effectivity of the classification models.