NDLI: Mixed Pixel Analysis for Flood Mapping Using Extended Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Dey, C. Xiuping Jia Fraser, D. Wang, L.
Copyright Year	2009
Abstract	This paper addresses the challenges of flood mapping using multispectral images. Quantitative flood mapping is critical for flood damage assessment and management. Remote sensing images obtained from various satellite or airborne sensors provide valuable data for this application, from which the information on the extent of flood can be extracted. However the great challenge involved in the data interpretation is to achieve more reliable flood extent mapping including both the fully inundated areas and the ‘wet’ areas where trees and houses are partly covered by water. This is a typical combined pure pixel and mixed pixel problem. In this paper, an extended Support Vector Machines method for spectral unmixing developed recently has been applied to generate an integrated map showing both pure pixels (fully inundated areas) and mixed pixels (trees and houses partly covered by water). The outputs were compared with the conventional mean based linear spectral mixture model, and better performance was demonstrated with a subset of Landsat ETM+ data recorded at the Daly River Basin, NT, Australia, on 3rd March, 2008, after a flood event.
Starting Page	291
Ending Page	295
File Size	1696977
Page Count	5
File Format	PDF
ISBN	9781424452972
DOI	10.1109/DICTA.2009.55
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Flood Mapping Multispectral imaging Extended Support Vector Machine Remote Sensing Rivers Floods Data mining Remote sensing Support vector machines Image sensors Satellites Vegetation mapping Australia
Content Type	Text
Resource Type	Article

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