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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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 |
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QA75 Electronic computers Computer science Mardani, Ichsan Non-negative matrix factorization for blind image separation |
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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 |
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1747818057469788160 |