Description: In this paper, an automatic near-real time (NRT) flood detection approach is presented, which combines histogram thresholding and segmentation based classification, specifically oriented to the analysis of single-polarized very high resolution Synthetic Aperture Radar (SAR) satellite data. The challenge of SAR-based flood detection is addressed in a completely unsupervised way, which assumes no training data and therefore no prior information about the class statistics to be available concerning the area of investigation. This is usually the case in NRT-disaster management, where the collection of ground truth information is not feasible due to time-constraints. A simple thresholding algorithm can be used in the most of the cases to distinguish between "flood" and "non-flood" pixels in a high resolution SAR image to detect the largest part of an inundation area. Due to the fact that local gray-level changes may not be distinguished by global thresholding techniques in large satellite scenes the thresholding algorithm is integrated into a split-based approach for the derivation of a global threshold by the analysis and combination of the split inherent information. The derived global threshold is then integrated into a multi-scale segmentation step combining the advantages of small-, medium- and large-scale per parcel segmentation. Experimental investigations performed on a TerraSAR-X Stripmap scene from southwest England during large scale flooding in the summer 2007 show high classification accuracies of the proposed split-based approach in combination with image segmentation and optional integration of digital elevation models.

Description: In this contribution, a hybrid multi-contextual Markov model for unsupervised near real-time flood detection in multi-temporal X-band synthetic aperture radar (SAR) data is presented. It incorporates scale-dependent, as well as spatio-temporal contextual information, into the classification scheme, by combining hierarchical marginal posterior mode (HMPM) estimation on directed graphs with noncausal Markov image modeling related to planar Markov random fields (MRFs). In order to increase computational performance, marginal posterior-based entropies are used for restricting the iterative bi-directional exchange of spatio-temporal information between consecutive images of a time sequence to objects exhibiting a low probability, to be classified correctly according to the HMPM estimation. The Markov models, originally developed for inference on regular graph structures of quadtrees and planar lattices, are adapted to the variable nature of irregular graphs, which are related to information driven image segmentation. Entropy based confidence maps, combined with spatio-temporal relationships of potentially inundated bright scattering vegetation to open water areas, are used for the quantification of the uncertainty in the labeling of each image element in flood possibility masks. With respect to accuracy and computational effort, experiments performed on a bi-temporal TerraSAR-X ScanSAR data-set from the Caprivi region of Namibia during flooding in 2009 and 2010 confirm the effectiveness of integrating hierarchical as well as spatio-temporal context into the labeling process, and of adapting the models to irregular graph structures.

Description: The near real-time provision of precise information about flood dynamics from synthetic aperture radar (SAR) data is an essential task in disaster management. A novel tile-based parametric thresholding approach under the generalized Gaussian assumption is applied on normalized change index data to automatically solve the three-class change detection problem in large-size images with small class a priori probabilities. The thresholding result is used for the initialization of a hybrid Markov model which integrates scale-dependent and spatiocontextual information into the labeling process by combining hierarchical with noncausal Markov image modeling. Hierarchical maximum a posteriori (HMAP) estimation using the Markov chains in scale, originally developed on quadtrees, is adapted to hierarchical irregular graphs. To reduce the computational effort of the iterative optimization process that is related to noncausal Markov models, a Markov random field (MRF) approach is defined, which is applied on a restricted region of the lowest level of the graph, selected according to the HMAP labeling result. The experiments that were performed on a bitemporal TerraSAR-X StripMap data set from South West England during and after a large-scale flooding in 2007 confirm the effectiveness of the proposed change detection method and show an increased classification accuracy of the hybrid MRF model in comparison to the sole application of the HMAP estimation. Additionally, the impact of the graph structure and the chosen model parameters on the labeling result as well as on the performance is discussed.

Description: Medium resolution SAR satellite data have been widely used for water and flood mapping in recent years. Since 2007 high resolution radar data with up to 1 m pixel spacing of the TerraSAR-X satellite are operationally available. The improved ground resolution of the system offers enormous potential for water detection. However, image analysis gets more challenging due to the large amount of image objects that are visible in the data. Water body detection methods are reviewed with regard to their applicability for TerraSAR-X data. Flood detection approaches for rapid disaster mapping are presented in this paper.

Description: Ziel von SAR-HQ war es, dedizierte Methoden zur Hochwasserdetektion und Schadensabschätzung zu entwickeln und hierbei die Anwendbarkeit von hochauflösenden X-Band Radardaten zu untersuchen und zu verbessern. Da Überflutungsereignisse in der Regel von starker Wolkenbedeckung begleitet werden, sind wetterunabhängige SAR (Synthetic Aperture Radar)-Fernerkundungsplattformen besonders geeignet um schnell, zuverlässig/wiederholbar und kostengünstig Informationen von Überschwemmungsgebieten zu erlangen. Obwohl bestehende C-Band gestützte Radarplattformen (ERS-2, ENVISAT ASAR, RADARSAT) ihre Nützlichkeit für die Kartierung großflächiger Hochwasserereignisse bereits bewiesen haben, weisen sie für die Extraktion von Hochwassermasken in komplexen bzw. kleinräumigen Szenarien, insbesondere urbanen Gebieten, deutliche Einschränkungen auf. Erst die neuen europäischen X-Band Radarsatelliten TerraSAR-X und Cosmo-SkyMed ermöglichen es, wetterunabhängig, räumlich flächendeckend und zeitlich wiederholbar Überschwemmungsflächen und Schäden in sehr hoher räumlicher Auflösung zu erfassen. Da eine effiziente Hochwasserkartierung Datenaufnahmen erfordert, die möglichst nahe am Zeitpunkt des maximalen Pegelstandes liegen, kann gerade die synergetische Nutzung mehrerer Satellitenplattformen die zeitliche Auflösung und Reaktionsfähigkeit entscheidend verbessern. Um den Erfordernissen und Möglichkeiten der neuen Radarsatelliten gerecht zu werden, wurden im Rahmen des Projektes angepasste Prozessierungs- und Analysetechniken für diese neue Gattung von Radarsatellitendaten entwickelt. Das Projekt wurde auf eine Einbindung der erstellten Methoden in operationelle Arbeitsabläufe der Datenprozessierung und Datenauswertung ausgerichtet, mit dem Ziel, eine schnelle und zuverlässige Bereitstellung von hochgenauen Kriseninformationen zu gewährleisten. Durch die Kombination von aus Radardaten abgeleiten Hochwassermasken mit zusätzlichen Datenquellen wie topographischen Karten oder digitalen Geländemodellen wurden Informationsprodukte generiert, die für Krisenmanagement, Risikoabschätzung und Wiederaufbau von zentraler Bedeutung sein können.