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| Titel |
Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data |
| VerfasserIn |
S. Martinis, A. Twele, S. Voigt |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 9, no. 2 ; Nr. 9, no. 2 (2009-03-11), S.303-314 |
| Datensatznummer |
250006701
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| Publikation (Nr.) |
 copernicus.org/nhess-9-303-2009.pdf |
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| Zusammenfassung |
| 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. |
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