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On independent component analysis applied to unmixing hyperspectral data

2004Conference object Restricted access
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dc.contributor.authorNascimento, Jose
dc.contributor.authorBioucas-Dias, José M.
dc.date.accessioned2016-05-27T11:26:16Z
dc.date.available2016-05-27T11:26:16Z
dc.date.issued2004
dc.description.abstractOne of the most challenging task underlying many hyperspectral imagery applications is the spectral unmixing, which decomposes a mixed pixel into a collection of reectance spectra, called endmember signatures, and their corresponding fractional abundances. Independent Component Analysis (ICA) have recently been proposed as a tool to unmix hyperspectral data. The basic goal of ICA is to nd a linear transformation to recover independent sources (abundance fractions) given only sensor observations that are unknown linear mixtures of the unobserved independent sources. In hyperspectral imagery the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be independent. This paper address hyperspectral data source dependence and its impact on ICA performance. The study consider simulated and real data. In simulated scenarios hyperspectral observations are described by a generative model that takes into account the degradation mechanisms normally found in hyperspectral applications. We conclude that ICA does not unmix correctly all sources. This conclusion is based on the a study of the mutual information. Nevertheless, some sources might be well separated mainly if the number of sources is large and the signal-to-noise ratio (SNR) is high.pt_PT
dc.identifier.citationNASCIMENTO, José M. P.; BIOUCAS-DIAS, José M. - On independent component analysis applied to unmixing hyperspectral data. Proceedings of SPIE - Image and Signal Processing for Remote Sensing IX. ISSN 0277-786X. Vol. 5238. pp. 306-315, 2004pt_PT
dc.identifier.doi10.1117/12.510652pt_PT
dc.identifier.issn0277-786X
dc.identifier.urihttp://hdl.handle.net/10400.21/6212
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSPIEpt_PT
dc.relationPOSI/34071/CPS/2000pt_PT
dc.subjectUnmixing hyperspectral datapt_PT
dc.subjectIndependent component analysispt_PT
dc.subjectMixture of Gaussianspt_PT
dc.titleOn independent component analysis applied to unmixing hyperspectral datapt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceBarcelona, Spainpt_PT
oaire.citation.endPage315pt_PT
oaire.citation.startPage306pt_PT
oaire.citation.titleProceedings of SPIE - Image and Signal Processing for Remote Sensing IXpt_PT
oaire.citation.volume5238pt_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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