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856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/350/347

100.1.#.a: Shaker, Ahmed; Yeung Yan, Wai; El Ashmawy, Nagwa

524.#.#.a: Shaker, Ahmed, et al. (2012). Panchromatic Satellite Image Classification for Flood Hazard Assessment. Journal of Applied Research and Technology; Vol. 10 Núm. 6. Recuperado de https://repositorio.unam.mx/contenidos/45592

245.1.0.a: Panchromatic Satellite Image Classification for Flood Hazard Assessment

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2012

264.#.1.c: 2012-12-01

653.#.#.a: Department of Civil Engineering; Ryerson University; Toronto; Ontario; Canada

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

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041.#.7.h: eng

520.3.#.a: The study aims to investigate the use of panchromatic (PAN) satellite image data for flood hazard assessment with anaid of various digital image processing techniques. Two SPOT PAN satellite images covering part of the Nile River inEgypt were used to delineate the flood extent during the years 1997 and 1998 (before and after a high flood). Threeclassification techniques, including the contextual classifier, maximum likelihood classifier and minimum distanceclassifier, were applied to the following 1) the original PAN image data, 2) the original PAN image data and grey-levelco-occurrence matrix texture created from the PAN data, and 3) the enhanced PAN image data using an edgesharpeningfilter. The classification results were assessed with reference to the results derived from manualdigitization and random checkpoints. Generally, the results showed improvement of the calculation of flood area whenan edge-sharpening filter was used. In addition, the maximum likelihood classifier yielded the best classificationaccuracy (up to 97%) compared to the other two classifiers. The research demonstrates the benefits of using PANsatellite imagery as a potential data source for flood hazard assessment.

773.1.#.t: Journal of Applied Research and Technology; Vol. 10 Núm. 6

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

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doi: https://doi.org/10.22201/icat.16656423.2012.10.6.350

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Artículo

Panchromatic Satellite Image Classification for Flood Hazard Assessment

Shaker, Ahmed; Yeung Yan, Wai; El Ashmawy, Nagwa

Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

Shaker, Ahmed, et al. (2012). Panchromatic Satellite Image Classification for Flood Hazard Assessment. Journal of Applied Research and Technology; Vol. 10 Núm. 6. Recuperado de https://repositorio.unam.mx/contenidos/45592

Descripción del recurso

Autor(es)
Shaker, Ahmed; Yeung Yan, Wai; El Ashmawy, Nagwa
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
Panchromatic Satellite Image Classification for Flood Hazard Assessment
Fecha
2012-12-01
Resumen
The study aims to investigate the use of panchromatic (PAN) satellite image data for flood hazard assessment with anaid of various digital image processing techniques. Two SPOT PAN satellite images covering part of the Nile River inEgypt were used to delineate the flood extent during the years 1997 and 1998 (before and after a high flood). Threeclassification techniques, including the contextual classifier, maximum likelihood classifier and minimum distanceclassifier, were applied to the following 1) the original PAN image data, 2) the original PAN image data and grey-levelco-occurrence matrix texture created from the PAN data, and 3) the enhanced PAN image data using an edgesharpeningfilter. The classification results were assessed with reference to the results derived from manualdigitization and random checkpoints. Generally, the results showed improvement of the calculation of flood area whenan edge-sharpening filter was used. In addition, the maximum likelihood classifier yielded the best classificationaccuracy (up to 97%) compared to the other two classifiers. The research demonstrates the benefits of using PANsatellite imagery as a potential data source for flood hazard assessment.
Tema
Department of Civil Engineering; Ryerson University; Toronto; Ontario; Canada
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

Enlaces