Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data
By S. Martinis, A. Twele, S. Voigt. 2009
454 citations recorded by the source corpus.
Abstract. 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.
Subfields: Aerospace Engineering, Global and Planetary Change, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/5d15f2779fddbe3aadc6315fa9825e213df6f3eb
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Citations in the corpus
- Sentinel-1-based flood mapping: a fully automated processing chain (2016, methodology): “Although the number of automatic flood mapping approaches has significantly increased over the last years, particularly in the SAR domain (e.g. Martinis, Twele, and Voigt 2009; Pulvirenti et al. 2011; Matgen et al. 2011; Westerhoff et al. 2013; Lu et al. 2014), a certain amount of manual user…”
- Flood extent mapping for Namibia using change detection and thresholding with SAR (2014, methodology): “Most SAR-based techniques for flood detection have been developed for monitoring large river flooding in the temperate northern latitudes; including the UK (Martinis et al 2009, Horritt et al 2001), Germany (Henry et al 2006) and eastern Europe (Gan et al 2012).”
- A Change Detection Approach to Flood Mapping in Urban Areas Using TerraSAR-X (2013, background): “…Bates are with the School of Geographical Sciences, University of Bristol, BS8 1SS Bristol, U.K. (email: guy.schumann@bristol.ac.uk; paul.bates@bristol.ac.uk) D. C. Mason is with the Environmental System Science Centre, University of Reading, RG6 6AL Reading, U.K. (email: dcm@mail.nerc-essc.ac.uk)”