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Flood boundary delineation from Synthetic Aperture Radar imagery using a statistical active contour model
Abstract
Flood extent maps derived from remotely sensed data can provide distributed validation data for hydraulic models of fluvial flow, and can be used for flood relief management and to develop spatially accurate hazard maps. A statistical active contour model is used to delineate a flood from the first European Remote Sensing satellite Synthetic Aperture Radar (ERS-1 SAR) imagery as a region of homogeneous speckle statistics. The segmentation uses both local tone and texture measures and is capable of accurate feature boundary representation. The results are assessed by comparison with simultaneous aerial photography, the SAR segmentation scheme classifying 75% by area of the shoreline region correctly. Seventy per cent of the shoreline coincides with the ground data to within 20 m. The main error is due to unflooded vegetation giving similar radar returns to open water.
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Citations
Cited by
Remote Sensing of Floods and Flood-Prone Areas: An Overview
Klemas, V. (2015) · Journal of Coastal Research · doi:10.2112/jcoastres-d-14-00160.1 · Cited by 220
Bates, P. D., Wilson, M. D., Horritt, M. S., Mason, D. C., Holden, N., & Currie, A. (2006) · Journal of Hydrology · doi:10.1016/j.jhydrol.2005.12.028 · Cited by 185
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Metadata
- Authors
- Horritt, M. S., Mason, D. C., & Luckman, A. J.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2001-01
- Volume
- 22
- Issue
- 13
- Pages
- 2489-2507
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W4241210177
- Cited by
- 274
- References
- 0
- FWCI
- 7.16
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
