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Hyperspectral signal subspace estimation

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dc.contributor.authorNascimento, Jose
dc.contributor.authorBioucas-Dias, José M.
dc.date.accessioned2016-05-02T16:15:01Z
dc.date.available2016-05-02T16:15:01Z
dc.date.issued2007
dc.description.abstractGiven an hyperspectral image, the determination of the number of endmembers and the subspace where they live without any prior knowledge is crucial to the success of hyperspectral image analysis. This paper introduces a new minimum mean squared error based approach to infer the signal subspace in hyperspectral imagery. The method, termed hyperspectral signal identification by minimum error (HySime), is eigendecomposition based and it does not depend on any tuning parameters. It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. 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 signal subspace estimation. IGARSS: 2007 IEEE International Geoscience and Remote Sensing Symposium, Vols 1-12: Sensing And Understanding Our Planet. ISSN 2153-6996. Vol. 1-12. 3225-3228, 2007pt_PT
dc.identifier.doi10.1109/IGARSS.2007.4423531pt_PT
dc.identifier.isbn978-1-4244-1211-2
dc.identifier.issn2153-6996
dc.identifier.urihttp://hdl.handle.net/10400.21/6140
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEE - Institute of Electrical and Electronics Engineers Inc.pt_PT
dc.relationOil Slick Surveillance Using ASAR and MERIS Data
dc.relation.ispartofseriesIEEE International Symposium on Geoscience and Remote Sensing IGARSS;
dc.subjectHyperspectral signalpt_PT
dc.subjectModelpt_PT
dc.titleHyperspectral signal subspace estimationpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleOil Slick Surveillance Using ASAR and MERIS Data
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/Orçamento de Funcionamento%2FPOSC/POSC%2FEEA-CPS%2F61271%2F2004/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/PDCTE/PDCTE%2FCPS%2F49967%2F2003/PT
oaire.citation.endPage3228pt_PT
oaire.citation.startPage3225pt_PT
oaire.citation.titleIGARSS: 2007 IEEE International Geoscience and Remote Sensing Symposium, Vols 1-12: Sensing And Understanding Our Planetpt_PT
oaire.citation.volume1-12pt_PT
oaire.fundingStreamOrçamento de Funcionamento/POSC
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.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsclosedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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relation.isAuthorOfPublication.latestForDiscoveryc7ffc6c0-1bdc-4f47-962a-a90dfb03073c
relation.isProjectOfPublication2f85bdd0-9810-446c-a063-c49fdc79e462
relation.isProjectOfPublication15c20117-8da0-4de1-9b37-eb381aeb5c54
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