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New developments on VCA unmixing algorithm

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dc.contributor.authorNascimento, Jose
dc.contributor.authorBioucas-Dias, José M.
dc.date.accessioned2016-05-27T10:52:50Z
dc.date.available2016-05-27T10:52:50Z
dc.date.issued2008
dc.description.abstractHyperspectral sensors are being developed for remote sensing applications. These sensors produce huge data volumes which require faster processing and analysis tools. Vertex component analysis (VCA) has become a very useful tool to unmix hyperspectral data. It has been successfully used to determine endmembers and unmix large hyperspectral data sets without the use of any a priori knowledge of the constituent spectra. Compared with other geometric-based approaches VCA is an efficient method from the computational point of view. In this paper we introduce new developments for VCA: 1) a new signal subspace identification method (HySime) is applied to infer the signal subspace where the data set live. This step also infers the number of endmembers present in the data set; 2) after the projection of the data set onto the signal subspace, the algorithm iteratively projects the data set onto several directions orthogonal to the subspace spanned by the endmembers already determined. The new endmember signature corresponds to these extreme of the projections. The capability of VCA to unmix large hyperspectral scenes (real or simulated), with low computational complexity, is also illustrated.pt_PT
dc.identifier.citationNASCIMENTO, José M. P.; BIOUCAS-DIAS, José M. - New developments on VCA unmixing algorithm. Proceedings of SPIE. ISSN 0277-786X. Vol. 7109. pp. 71090F-1-71090F-9, 2008pt_PT
dc.identifier.doi10.1117/12.799838pt_PT
dc.identifier.issn0277-786X
dc.identifier.urihttp://hdl.handle.net/10400.21/6209
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.subjectVertex component analysispt_PT
dc.subjectUnsupervised unmixingpt_PT
dc.subjectHyperspectral datapt_PT
dc.subjectLinear Mixturespt_PT
dc.titleNew developments on VCA unmixing algorithmpt_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.conferencePlaceCardiff, United Kingdompt_PT
oaire.citation.endPage71090F-9pt_PT
oaire.citation.startPage71090F-1pt_PT
oaire.citation.titleProceedings of SPIE - Image and Signal Processing for Remote Sensing XIVpt_PT
oaire.citation.volume7109pt_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
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relation.isAuthorOfPublication.latestForDiscoveryc7ffc6c0-1bdc-4f47-962a-a90dfb03073c
relation.isProjectOfPublication15c20117-8da0-4de1-9b37-eb381aeb5c54
relation.isProjectOfPublication.latestForDiscovery15c20117-8da0-4de1-9b37-eb381aeb5c54

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