Mixed pixel analysis and assessment for flood mapping

Sarker, Chandrama (2014) Mixed pixel analysis and assessment for flood mapping. Masters by Research thesis, University of New South Wales.

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Abstract

The most difficult operation in flood inundation mapping using optical flood images is to map the ‘wet’ areas where trees and houses are partly covered by water. This can be referred to as a typical problem of the presence of mixed pixels in the images. A number of automatic information extracting image classification algorithms have been developed over the years for flood mapping using optical remote sensing images, with most labelling a pixel as a particular class. However, they often fail to generate reliable flood inundation mapping because of the presence of mixed pixels in the images. To solve this problem, spectral unmixing methods have been developed. In this thesis, methods for selecting endmembers and the method to model the primary classes for unmixing, the two most important issues in spectral unmixing, are investigated.

We conduct comparative studies of three typical spectral unmixing algorithms, Partial Constrained Linear Spectral unmixing, Multiple Endmember Selection Mixture Analysis and spectral unmixing using the Extended Support Vector Machine method. They are analysed and assessed by error analysis in flood mapping using MODIS, Landsat and World View-2 images. The Conventional Root Mean Square Error Assessment is applied to obtain errors for estimated fractions of each primary class. Moreover, a newly developed Fuzzy Error Matrix is used to obtain a clear picture of error distributions at the pixel level. This thesis shows that the Extended Support Vector Machine method is able to provide a more reliable estimation of fractional abundances and allows the use of a complete set of training samples to model a defined pure class. Furthermore, it can be applied to analysis of both pure and mixed pixels to provide integrated hard-soft classification results.

Our research also identifies and explores a serious drawback in relation to endmember selections in current spectral unmixing methods which apply fixed sets of endmember classes or pure classes for mixture analysis of every pixel in an entire image. However, as it is not accurate to assume that every pixel in an image must contain all endmember classes, these methods usually cause an over-estimation of the fractional abundances in a particular pixel. In this thesis, a subset of adaptive endmembers in every pixel is derived using the proposed methods to form an endmember index matrix. The experimental results show that using the pixel-dependent endmembers in unmixing significantly improves performance.

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ID Code: 95596
Item Type: Thesis (Masters by Research)
Refereed: No
Additional Information: In the Public version of thesis the figure 1.1 and table 1.1 and 1.2 has been removed for copyright purposes. Please refer the weblink for viewing the public version pdf.
Keywords: Mixed Pixel Analysis, Flood Mapping, SVM classification, Linear Spectral Unmixing
Subjects: Australian and New Zealand Standard Research Classification > EARTH SCIENCES (040000) > PHYSICAL GEOGRAPHY AND ENVIRONMENTAL GEOSCIENCE (040600) > Natural Hazards (040604)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > GEOMATIC ENGINEERING (090900) > Cartography (090901)
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 > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Institution: University of New South Wales
Copyright Owner: This work is in copyright and subject to the protections of the Copyright Act 1968.
Please see additional information at https://www.library.unsw.edu.au/copyright/unsworks.html
Copyright Statement: This work can be used in accordance with the Creative Commons BY-NC-ND license.
Please see additional information at https://www.library.unsw.edu.au/copyright/unsworks.html
Deposited On: 16 May 2016 23:37
Last Modified: 16 May 2016 23:37

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