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Dependent component analysis: a hyperspectral unmixing algorithm

dc.contributor.authorNascimento, Jose
dc.contributor.authorBioucas-Dias, José M.
dc.date.accessioned2014-06-05T10:28:50Z
dc.date.available2014-06-05T10:28:50Z
dc.date.issued2007-06
dc.description.abstractLinear unmixing decomposes a hyperspectral image into a collection of reflectance spectra of the materials present in the scene, called endmember signatures, and the corresponding abundance fractions at each pixel in a spatial area of interest. This paper introduces a new unmixing method, called Dependent Component Analysis (DECA), which overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical properties of hyperspectral data. DECA models the abundance fractions as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. The performance of the method is illustrated using simulated and real data.por
dc.identifier.citationNASCIMENTO, José M. P.; BIOUCAS-DIAS, José M. - Dependent Component Analysis: A Hyperspectral Unmixing Algorithm. Pattern Recognition and Image Analysis. Vol. 4478 (2007), p. 612-619.por
dc.identifier.isbn978-3-540-72848-1
dc.identifier.isbn978-3-540-72849-8
dc.identifier.other10.1007/978-3-540-72849-8_77
dc.identifier.urihttp://hdl.handle.net/10400.21/3611
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherSpringer Berlin Heidelbergpor
dc.relation.ispartofseriesLecture Notes in Computer Science;
dc.relation.publisherversionhttp://link.springer.com/chapter/10.1007%2F978-3-540-72849-8_77por
dc.subjectHyperspectral unmixing algorithmpor
dc.titleDependent component analysis: a hyperspectral unmixing algorithmpor
dc.typebook part
dspace.entity.typePublication
oaire.citation.conferencePlaceGironapor
oaire.citation.endPage619por
oaire.citation.startPage612por
oaire.citation.titlePattern Recognition and Image Analysispor
oaire.citation.volume4478por
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
rcaap.rightsrestrictedAccesspor
rcaap.typebookPartpor
relation.isAuthorOfPublicationc7ffc6c0-1bdc-4f47-962a-a90dfb03073c
relation.isAuthorOfPublication.latestForDiscoveryc7ffc6c0-1bdc-4f47-962a-a90dfb03073c

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