Non-negative matrix factorization for blind image separation

Hyperspectral unmixing is a process to identify the constituent materials and estimate the corresponding fractions from the mixture, nonnegative matrix factions ( NMF ) is suitable as a candidate for the linear spectral mixture mode, has been applied to the unmixing hyperspectral data. Unfortunately...

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Main Author:	Mardani, Ichsan
Format:	Thesis
Language:	English
Published:	2014
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/78731/1/IchsanMardaniMFC2014.pdf
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id	my-utm-ep.78731
record_format	uketd_dc
spelling	my-utm-ep.787312018-08-30T08:06:45Z Non-negative matrix factorization for blind image separation 2014-05 Mardani, Ichsan QA75 Electronic computers. Computer science Hyperspectral unmixing is a process to identify the constituent materials and estimate the corresponding fractions from the mixture, nonnegative matrix factions ( NMF ) is suitable as a candidate for the linear spectral mixture mode, has been applied to the unmixing hyperspectral data. Unfortunately, the local minima is cause by the nonconvexity of the objective function makes the solution nonunique, thus only the nonnegativity constraint is not sufficient enough to lead to a well define problems. Therefore, two inherent characteristic of hyperspectal data, piecewise smoothness ( both temporal and spatial ) of spectral data and sparseness of abundance fraction of every material, are introduce to the NMF. The adaptive potential function from discontinuity adaptive Markov random field model is used to describe the smoothness constraint while preserving discontinuities is spectral data. At the same time two NMF algorithms, non smooth NMS and NMF with sparseness constraint, are used to quantify the degree of sparseness of material abundances. Experiment using the synthetic and real data demonstrate the proposed algorithms provides an effective unsupervised technique for hyperspectial unmixing 2014-05 Thesis http://eprints.utm.my/id/eprint/78731/ http://eprints.utm.my/id/eprint/78731/1/IchsanMardaniMFC2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:105910 masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Mardani, Ichsan Non-negative matrix factorization for blind image separation
description	Hyperspectral unmixing is a process to identify the constituent materials and estimate the corresponding fractions from the mixture, nonnegative matrix factions ( NMF ) is suitable as a candidate for the linear spectral mixture mode, has been applied to the unmixing hyperspectral data. Unfortunately, the local minima is cause by the nonconvexity of the objective function makes the solution nonunique, thus only the nonnegativity constraint is not sufficient enough to lead to a well define problems. Therefore, two inherent characteristic of hyperspectal data, piecewise smoothness ( both temporal and spatial ) of spectral data and sparseness of abundance fraction of every material, are introduce to the NMF. The adaptive potential function from discontinuity adaptive Markov random field model is used to describe the smoothness constraint while preserving discontinuities is spectral data. At the same time two NMF algorithms, non smooth NMS and NMF with sparseness constraint, are used to quantify the degree of sparseness of material abundances. Experiment using the synthetic and real data demonstrate the proposed algorithms provides an effective unsupervised technique for hyperspectial unmixing
format	Thesis
qualification_level	Master's degree
author	Mardani, Ichsan
author_facet	Mardani, Ichsan
author_sort	Mardani, Ichsan
title	Non-negative matrix factorization for blind image separation
title_short	Non-negative matrix factorization for blind image separation
title_full	Non-negative matrix factorization for blind image separation
title_fullStr	Non-negative matrix factorization for blind image separation
title_full_unstemmed	Non-negative matrix factorization for blind image separation
title_sort	non-negative matrix factorization for blind image separation
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
url	http://eprints.utm.my/id/eprint/78731/1/IchsanMardaniMFC2014.pdf
_version_	1747818057469788160

Non-negative matrix factorization for blind image separation

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