Flood mapping of Northern Peninsular Malaysia using SAR images

Malaysia is one of the evident countries whose recurrence of floods proves that floods are getting worse. The northern peninsular of Malaysia has lost a lot of lives and property worth billions to the series of floods that have been occurring for many years. Many disaster management strategies have...

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Main Author:	Dutsenwai, Hafsat Saleh
Format:	Thesis
Language:	English
Published:	2014
Subjects:	G70.39-70.6 Remote sensing
Online Access:	http://eprints.utm.my/id/eprint/51412/25/HafsatSalehDutsenwaiMFGHT2014.pdf
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id	my-utm-ep.51412
record_format	uketd_dc
spelling	my-utm-ep.514122020-07-13T03:55:21Z Flood mapping of Northern Peninsular Malaysia using SAR images 2014-08 Dutsenwai, Hafsat Saleh G70.39-70.6 Remote sensing Malaysia is one of the evident countries whose recurrence of floods proves that floods are getting worse. The northern peninsular of Malaysia has lost a lot of lives and property worth billions to the series of floods that have been occurring for many years. Many disaster management strategies have been adopted by the Malaysian government in handling these flood disasters but it is still a topic in the annual agenda. This research project aimed at using fusion techniques in mapping the flood extents in the northern peninsular Malaysia in order to contribute to the flood disaster eradication by extracting more and better information through the fusion of RadarSat 1 and TerraSAR-X images. The Principal Component Analysis was also used and compared with the fusion techniques which include the Hue Saturation and Value (HSV), the Brovey Transformation (BT), the Gram Schmidt (GS), and the Principal Component Spectral Sharpening (PCSS). The best principal component of the PCA, that is the PC2 which classified and compared with the classification of the other fusion techniques using Maximum likelihood (ML) and support Vector Machine (SVM). The results indicated BT technique has the highest overall accuracy of 70.9615% and kappa coefficient of 0.3418. This method showed relative improvement on the classification of the flooded and non-flooded area which was used to produce the flood extent Map that was further validated with the DEM data. The final results in this study showed that more information on the areas that are affected by the floods especially the extents, became more exposed after the classification of the fused images. 2014-08 Thesis http://eprints.utm.my/id/eprint/51412/ http://eprints.utm.my/id/eprint/51412/25/HafsatSalehDutsenwaiMFGHT2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:86579 masters Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate Faculty of Geoinformation and Real Estate
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	G70.39-70.6 Remote sensing
spellingShingle	G70.39-70.6 Remote sensing Dutsenwai, Hafsat Saleh Flood mapping of Northern Peninsular Malaysia using SAR images
description	Malaysia is one of the evident countries whose recurrence of floods proves that floods are getting worse. The northern peninsular of Malaysia has lost a lot of lives and property worth billions to the series of floods that have been occurring for many years. Many disaster management strategies have been adopted by the Malaysian government in handling these flood disasters but it is still a topic in the annual agenda. This research project aimed at using fusion techniques in mapping the flood extents in the northern peninsular Malaysia in order to contribute to the flood disaster eradication by extracting more and better information through the fusion of RadarSat 1 and TerraSAR-X images. The Principal Component Analysis was also used and compared with the fusion techniques which include the Hue Saturation and Value (HSV), the Brovey Transformation (BT), the Gram Schmidt (GS), and the Principal Component Spectral Sharpening (PCSS). The best principal component of the PCA, that is the PC2 which classified and compared with the classification of the other fusion techniques using Maximum likelihood (ML) and support Vector Machine (SVM). The results indicated BT technique has the highest overall accuracy of 70.9615% and kappa coefficient of 0.3418. This method showed relative improvement on the classification of the flooded and non-flooded area which was used to produce the flood extent Map that was further validated with the DEM data. The final results in this study showed that more information on the areas that are affected by the floods especially the extents, became more exposed after the classification of the fused images.
format	Thesis
qualification_level	Master's degree
author	Dutsenwai, Hafsat Saleh
author_facet	Dutsenwai, Hafsat Saleh
author_sort	Dutsenwai, Hafsat Saleh
title	Flood mapping of Northern Peninsular Malaysia using SAR images
title_short	Flood mapping of Northern Peninsular Malaysia using SAR images
title_full	Flood mapping of Northern Peninsular Malaysia using SAR images
title_fullStr	Flood mapping of Northern Peninsular Malaysia using SAR images
title_full_unstemmed	Flood mapping of Northern Peninsular Malaysia using SAR images
title_sort	flood mapping of northern peninsular malaysia using sar images
granting_institution	Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate
granting_department	Faculty of Geoinformation and Real Estate
publishDate	2014
url	http://eprints.utm.my/id/eprint/51412/25/HafsatSalehDutsenwaiMFGHT2014.pdf
_version_	1747817549128531968

Flood mapping of Northern Peninsular Malaysia using SAR images

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