Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/25107
Campo DC Valoridioma
dc.contributor.authorIbarrola-Ulzurrun, E.en_US
dc.contributor.authorMarcello, Javieren_US
dc.contributor.authorGonzalo Martin,Consueloen_US
dc.date.accessioned2018-01-13T03:31:21Z-
dc.date.accessioned2018-04-25T13:49:02Z-
dc.date.available2018-01-13T03:31:21Z-
dc.date.available2018-04-25T13:49:02Z-
dc.date.issued2017en_US
dc.identifier.issn1099-4300en_US
dc.identifier.urihttp://hdl.handle.net/10553/30709-
dc.description.abstractHyperspectral imagery (HSI) integratesmany continuous and narrowbands that cover different regions of the electromagnetic spectrum. However, the main challenge is the high dimensionality of HSI data due to the 'Hughes' phenomenon. Thus, dimensionality reduction is necessary before applying classification algorithms to obtain accurate thematic maps. We focus the study on the following feature-extraction algorithms: Principal Component Analysis (PCA),MinimumNoise Fraction (MNF), and Independent Component Analysis (ICA). After a literature survey, we have observed a lack of a comparative study on these techniques as well as accurate strategies to determine the number of components. Hence, the first objective was to compare traditional dimensionality reduction techniques (PCA, MNF, and ICA) in HSI of the Compact Airborne Spectrographic Imager (CASI) sensor and to evaluate different strategies for selecting the most suitable number of components in the transformed space. The second objective was to determine a new dimensionality reduction approach by dividing the CASIHSI regarding the spectral regions covering the electromagnetic spectrum. The components selected fromthe transformed space of the different spectral regions were stacked. This stacked transformed space was evaluated to see if the proposed approach improves the final classification.-
dc.formatapplication/pdf-
dc.languageengen_US
dc.relation.ispartofEntropyen_US
dc.rightsby-nc-nd-
dc.sourceEntropy [ISSN 1099-4300], v. 19 (12), article number 666en_US
dc.subject220921 Espectroscopia-
dc.subject330412 Dispositivos de control-
dc.subject220990 Tratamiento digital. Imágenes-
dc.subject.otherRemote sensing-
dc.subject.otherHyperspectral sensor-
dc.subject.otherFeature-extraction-
dc.subject.otherTexture measurement-
dc.subject.otherClassification-
dc.subject.otherEcosystem management-
dc.titleAssessment of component selection strategies in hyperspectral imageryen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/e19120666
dc.identifier.scopus85038369900
dc.identifier.isi000419007900035-
dc.identifier.urlhttp://api.elsevier.com/content/abstract/scopus_id/85038369900-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.authorscopusid57193098496
dc.contributor.authorscopusid6602158797
dc.contributor.authorscopusid36561411500
dc.identifier.crisid-;473;--
dc.identifier.eissn1099-4300-
dc.identifier.issue12-
dc.relation.volume19-
dc.investigacionIngeniería y Arquitectura-
dc.project.referenceARTEMISAT-2 (CTM2016-77733-R); FPI Grant (BES-2014-069426)-
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess-
dc.source.typear-
dc.type2Artículoen_US
dc.identifier.external473-
dc.identifier.externalWOS:000419007900035-
dc.identifier.ulpgces
dc.description.sjr0,592
dc.description.jcr2,305
dc.description.sjrqQ2
dc.description.jcrqQ2
dc.description.scieSCIE
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR IOCAG: Procesado de Imágenes y Teledetección-
crisitem.author.deptIU de Oceanografía y Cambio Global-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0001-5062-7491-
crisitem.author.orcid0000-0002-9646-1017-
crisitem.author.parentorgIU de Oceanografía y Cambio Global-
crisitem.author.fullNameIbarrola Ulzurrun, Edurne-
crisitem.author.fullNameMarcello Ruiz, Francisco Javier-
crisitem.author.fullNameGonzalo Martin,Consuelo-
Colección:Artículos
miniatura
Adobe PDF (8,3 MB)
Vista resumida

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.