Paper Info
Title
Mixed Pixel Analysis for Flood Mapping Using Extended Support Vector Machine
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.
Year
DOI
Venue
2009
10.1109/DICTA.2009.55
DICTA
Keywords
Field
DocType
floods,geophysical image processing,image resolution,remote sensing,support vector machines,Landsat ETM+ data,extended support vector machine,flood damage assessment,flood management,flood mapping,linear spectral mixture model,mixed pixel analysis,multispectral images,remote sensing images,Extended Support Vector Machine,Flood Mapping,Remote Sensing
Computer vision,Satellite,Computer science,Multispectral image,Support vector machine,Pixel,Artificial intelligence,Statistical classification,Image resolution,Flood myth,Mixture model
Conference
Citations 
PageRank 
References 
1
0.39
2
Authors
4
Name
Order
Citations
PageRank
Chandrama Dey110.73
Xiuping Jia21424126.54
D. Fraser310.39
Lipo Wang42784338.57