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Hyperspectral unmixing algorithm via dependent component analysis

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dc.contributor.authorNascimento, Jose
dc.contributor.authorBioucas-Dias, José M.
dc.date.accessioned2016-05-04T11:41:30Z
dc.date.available2016-05-04T11:41:30Z
dc.date.issued2007
dc.description.abstractThis paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel. DECA assumes that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abudances are modeled 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. This method overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.pt_PT
dc.identifier.citationNASCIMENTO, José M. P.; BIOUCAS-DIAS, José M. - Hyperspectral unmixing algorithm via dependent component analysis. IGARSS: 2007 IEEE International Geoscience and Remote Sensing Symposium, Vols 1-12: Sensing And Understanding Our Planet. ISBN 978-14244-1211-2. 4033-4036, 2007pt_PT
dc.identifier.doi10.1109/IGARSS.2007.4423734pt_PT
dc.identifier.isbn978-1-4244-1211-2
dc.identifier.urihttp://hdl.handle.net/10400.21/6153
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEE-Institute Electrical Electronics Engineers INC.pt_PT
dc.relation.ispartofseriesIEEE International Symposium on Geoscience and Remote Sensing (IGARSS);
dc.subjectExpectation-maximisation algorithmpt_PT
dc.subjectGeophysical techniquespt_PT
dc.subjectGeophysics computingpt_PT
dc.subjectDirichlet densitiespt_PT
dc.subjectAbundance fractionspt_PT
dc.subjectDependent component analysispt_PT
dc.subjectGeneralized expectation-maximization algorithmpt_PT
dc.subjectHyperspectral unmixing algorithmpt_PT
dc.subjectAlgorithm design and analysispt_PT
dc.subjectHyperspectral imagingpt_PT
dc.subjectHyperspectral sensorspt_PT
dc.subjectIcept_PT
dc.subjectIndependent component analysispt_PT
dc.subjectInfrared image sensorspt_PT
dc.subjectLaboratoriespt_PT
dc.subjectLayoutpt_PT
dc.subjectReflectivitypt_PT
dc.subjectTelecommunicationspt_PT
dc.titleHyperspectral unmixing algorithm via dependent component analysispt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.endPage4036pt_PT
oaire.citation.startPage4033pt_PT
oaire.citation.titleIGARSS: 2007 IEEE International Geoscience and Remote Sensing Symposium, Vols 1-12: Sensing And Understanding Our Planetpt_PT
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.rightsclosedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationc7ffc6c0-1bdc-4f47-962a-a90dfb03073c
relation.isAuthorOfPublication.latestForDiscoveryc7ffc6c0-1bdc-4f47-962a-a90dfb03073c

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