In this paper, a fully automated processing chain for near real-time flood detection using high resolution TerraSAR-X Synthetic Aperture Radar (SAR) data is presented. The processing chain including SAR data pre-processing, computation and adaption of global auxiliary data, unsupervised initialization of the classification as well as post-classification refinement by using a fuzzy logic-based approach is automatically triggered after satellite data delivery. The dissemination of flood maps resulting from this service is performed through an online service which can be activated on-demand for emergency response purposes (i.e., when a flood situation evolves). The classification methodology is based on previous work of the authors but was substantially refined and extended for robustness and transferability to guarantee high classification accuracy under different environmental conditions and sensor configurations. With respect to accuracy and computational effort, experiments performed on a data set of 175 different TerraSAR-X scenes acquired during flooding all over the world with different sensor configurations confirm the robustness and effectiveness of the proposed flood mapping service. These promising results have been further confirmed by means of an in-depth validation performed for three study sites in Germany, Thailand, and Albania/Montenegro. (c) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
