Mixed pixel analysis for flood mapping using extended support vector machine

Sarker, Chandrama, Jia, Xiuping, Wang, Liguo, & Fraser, Donald (2009) Mixed pixel analysis for flood mapping using extended support vector machine. In Proceedings of Digital Image Computing: Techniques and Applications, 2009. DICTA '09., IEEE, Melbourne, Victoria, pp. 291-295.

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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.

Impact and interest:

1 citations in Scopus
1 citations in Web of Science®
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ID Code: 95458
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Extended Support Vector Machine, Flood Mapping, Remote Sensing
DOI: 10.1109/DICTA.2009.55
ISBN: 9781424452972
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > GEOMATIC ENGINEERING (090900) > Photogrammetry and Remote Sensing (090905)
Divisions: Current > Research Centres > ARC Centre of Excellence for Robotic Vision
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2009 IEEE
Copyright Statement: Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Deposited On: 10 May 2016 02:41
Last Modified: 26 Jun 2017 14:46

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