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Blind hyperspectral unmixing

2007-09Conference object Restricted access
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dc.contributor.authorNascimento, Jose
dc.contributor.authorBioucas-Dias, José M.
dc.date.accessioned2016-05-03T13:40:40Z
dc.date.available2016-05-03T13:40:40Z
dc.date.issued2007-09
dc.description.abstractHyperspectral unmixing methods aim at the decomposition of a hyperspectral image into a collection endmember signatures, i.e., the radiance or reflectance of the materials present in the scene, and the correspondent abundance fractions at each pixel in the image. This paper introduces a new unmixing method termed dependent component analysis (DECA). This method is blind and fully automatic and it overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. DECA is based on the linear mixture model, i.e., each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abundances are modeled as mixtures of Dirichlet densities, thus enforcing the non-negativity and constant sum constraints, imposed by the acquisition process. The endmembers signatures are inferred by a generalized expectation-maximization (GEM) type algorithm. The paper illustrates the effectiveness of DECA on synthetic and real hyperspectral images.pt_PT
dc.identifier.citationNASCIMENTO, José M. P.; BIOUCAS-DIAS, José M. - Blind hyperspectral unmixing. Image and Signal Processing for Remote Sensing XIII - Proceedings of SPIE. ISSN 0277-786X. Vol. 6748. 67480J-1- 67480J8, 2007pt_PT
dc.identifier.doi10.1117/12.738158pt_PT
dc.identifier.issn0277-786X
dc.identifier.urihttp://hdl.handle.net/10400.21/6148
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSPIEpt_PT
dc.relationPOSC/EEACPS/ 61271/2004pt_PT
dc.relationOil Slick Surveillance Using ASAR and MERIS Data
dc.subjectDependent component analysispt_PT
dc.subjectUnsupervised unmixingpt_PT
dc.subjectHyperspectral datapt_PT
dc.subjectLinear mixturept_PT
dc.subjectModelpt_PT
dc.subjectSimplexpt_PT
dc.titleBlind hyperspectral unmixingpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleOil Slick Surveillance Using ASAR and MERIS Data
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/PDCTE/PDCTE%2FCPS%2F49967%2F2003/PT
oaire.citation.endPage67480J-8pt_PT
oaire.citation.startPage67480J-1pt_PT
oaire.citation.titleImage and Signal Processing for Remote Sensing XIII - Proceedings of SPIEpt_PT
oaire.citation.volume6748pt_PT
oaire.fundingStreamPDCTE
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.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationc7ffc6c0-1bdc-4f47-962a-a90dfb03073c
relation.isAuthorOfPublication.latestForDiscoveryc7ffc6c0-1bdc-4f47-962a-a90dfb03073c
relation.isProjectOfPublication15c20117-8da0-4de1-9b37-eb381aeb5c54
relation.isProjectOfPublication.latestForDiscovery15c20117-8da0-4de1-9b37-eb381aeb5c54

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