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Article: PSO-EM: A hyperspectral unmixing algorithm based on normal compositional model

TitlePSO-EM: A hyperspectral unmixing algorithm based on normal compositional model
Authors
KeywordsExpectation maximization (EM) algorithm
particle swarm optimization (PSO)
normal compositional model (NCM)
hyperspectral unmixing
Issue Date2014
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2014, v. 52, n. 12, p. 7782-7792 How to Cite?
AbstractA new hyperspectral unmixing algorithm is proposed based on the normal compositional model (NCM) to estimate the endmembers and abundance parameters jointly in this paper. The NCM considers the hyperspectral imaging as a stochastic process and interprets each pixel value as a random vector, which is linearly mixed by the endmembers. More precisely, these endmembers are also treated as random variables as opposed to deterministic values in order to capture spectral variability that is not well described by the linear mixing model (LMM). However, the higher complexity of such an unmixing model leads to more difficulty in parameter estimation. A particle swarm optimization- expectation maximization (PSO-EM) algorithm, a "winner-take-all" version of the EM, is proposed to solve the parameter estimation problem, which employs a partial E step. The main contribution of the proposed PSO-EM is making optimum use of particle swarm optimization method (PSO) in the partial E step, which solves the difficulty of the integrals in the NCM model. The performance of the proposed methodology is evaluated through synthetic and real data experiments. Our obtained results demonstrate the superior performance of PSO-EM compared to other NCM-based as well as LMM-based methods. © 2014 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/298081
ISSN
2020 Impact Factor: 5.6
2020 SCImago Journal Rankings: 2.141
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, Bing-
dc.contributor.authorZhuang, Lina-
dc.contributor.authorGao, Lianru-
dc.contributor.authorLuo, Wenfei-
dc.contributor.authorRan, Qiong-
dc.contributor.authorDu, Qian-
dc.date.accessioned2021-04-08T03:07:38Z-
dc.date.available2021-04-08T03:07:38Z-
dc.date.issued2014-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2014, v. 52, n. 12, p. 7782-7792-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/298081-
dc.description.abstractA new hyperspectral unmixing algorithm is proposed based on the normal compositional model (NCM) to estimate the endmembers and abundance parameters jointly in this paper. The NCM considers the hyperspectral imaging as a stochastic process and interprets each pixel value as a random vector, which is linearly mixed by the endmembers. More precisely, these endmembers are also treated as random variables as opposed to deterministic values in order to capture spectral variability that is not well described by the linear mixing model (LMM). However, the higher complexity of such an unmixing model leads to more difficulty in parameter estimation. A particle swarm optimization- expectation maximization (PSO-EM) algorithm, a "winner-take-all" version of the EM, is proposed to solve the parameter estimation problem, which employs a partial E step. The main contribution of the proposed PSO-EM is making optimum use of particle swarm optimization method (PSO) in the partial E step, which solves the difficulty of the integrals in the NCM model. The performance of the proposed methodology is evaluated through synthetic and real data experiments. Our obtained results demonstrate the superior performance of PSO-EM compared to other NCM-based as well as LMM-based methods. © 2014 IEEE.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.subjectExpectation maximization (EM) algorithm-
dc.subjectparticle swarm optimization (PSO)-
dc.subjectnormal compositional model (NCM)-
dc.subjecthyperspectral unmixing-
dc.titlePSO-EM: A hyperspectral unmixing algorithm based on normal compositional model-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2014.2319337-
dc.identifier.scopuseid_2-s2.0-84903280178-
dc.identifier.volume52-
dc.identifier.issue12-
dc.identifier.spage7782-
dc.identifier.epage7792-
dc.identifier.isiWOS:000341532100027-
dc.identifier.issnl0196-2892-

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