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Hyperspectral imagery framework for unmixing and dimensionality estimation

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dc.contributor.authorNascimento, Jose
dc.contributor.authorBioucas-Dias, José M.
dc.date.accessioned2016-05-02T17:09:05Z
dc.date.available2016-05-02T17:09:05Z
dc.date.issued2013
dc.description.abstractIn hyperspectral imagery a pixel typically consists mixture of spectral signatures of reference substances, also called endmembers. Linear spectral mixture analysis, or linear unmixing, aims at estimating the number of endmembers, their spectral signatures, and their abundance fractions. This paper proposes a framework for hyperpsectral unmixing. A blind method (SISAL) is used for the estimation of the unknown endmember signature and their abundance fractions. This method solve a non-convex problem by a sequence of augmented Lagrangian optimizations, where the positivity constraints, forcing the spectral vectors to belong to the convex hull of the endmember signatures, are replaced by soft constraints. The proposed framework simultaneously estimates the number of endmembers present in the hyperspectral image by an algorithm based on the minimum description length (MDL) principle. Experimental results on both synthetic and real hyperspectral data demonstrate the effectiveness of the proposed algorithm.pt_PT
dc.identifier.citationNASCIMENTO, José M. P.; BIOUCAS-DIAS, José M. - Hyperspectral imagery framework for unmixing and dimensionality estimation. Pattern Recognition - Applications and Methods. ISBN 978-3-642-36529-4. Vol. 204. 193-204, 2013pt_PT
dc.identifier.doi10.1007/978-3-642-36530-0_16pt_PT
dc.identifier.isbn978-3-642-36529-4
dc.identifier.isbn978-3-642-36530-0
dc.identifier.issn2194-5357
dc.identifier.issn2194-5365
dc.identifier.urihttp://hdl.handle.net/10400.21/6143
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer-Verlagpt_PT
dc.relationStrategic Project - LA 8 - 2011-2012
dc.relation.ispartofseriesAdvances in Intelligent Systems and Computing;
dc.subjectBlind hyperspectral unmixingpt_PT
dc.subjectMinimum volume simplexpt_PT
dc.subjectMinimum description lengthpt_PT
dc.subjectMDLpt_PT
dc.subjectVariable splitting augmented lagrangianpt_PT
dc.subjectDimensionality reductionpt_PT
dc.titleHyperspectral imagery framework for unmixing and dimensionality estimationpt_PT
dc.typebook part
dspace.entity.typePublication
oaire.awardTitleStrategic Project - LA 8 - 2011-2012
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/PEst-OE%2FEEI%2FLA0008%2F2011/PT
oaire.citation.endPage204pt_PT
oaire.citation.startPage193pt_PT
oaire.citation.titlePattern Recognition - Applications and Methodspt_PT
oaire.citation.volume204pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameNascimento
person.givenNameJose
person.identifier.ciencia-id6912-6F61-1964
person.identifier.orcid0000-0002-5291-6147
person.identifier.ridE-6212-2015
person.identifier.scopus-author-id55920018000
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsclosedAccesspt_PT
rcaap.typebookPartpt_PT
relation.isAuthorOfPublicationc7ffc6c0-1bdc-4f47-962a-a90dfb03073c
relation.isAuthorOfPublication.latestForDiscoveryc7ffc6c0-1bdc-4f47-962a-a90dfb03073c
relation.isProjectOfPublication9fabd534-dda1-43e9-928f-4f085227453a
relation.isProjectOfPublication.latestForDiscovery9fabd534-dda1-43e9-928f-4f085227453a

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