Fourier–Mellin registration of two hyperspectral images

dc.contributor.affiliationUniversidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías da Informacióngl
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Electrónica e Computacióngl
dc.contributor.areaÁrea de Enxeñaría e Arquitectura
dc.contributor.authorOrdóñez Iglesias, Álvaro
dc.contributor.authorArgüello Pedreira, Francisco
dc.contributor.authorBlanco Heras, Dora
dc.date.accessioned2018-12-05T08:22:22Z
dc.date.available2018-12-05T08:22:22Z
dc.date.issued2017-03-21
dc.description.abstractHyperspectral images contain a great amount of information which can be used to more robustly register such images. In this article, we present a phase correlation method to register two hyperspectral images that takes into account their multiband structure. The proposed method is based on principal component analysis, the multilayer fractional Fourier transform, a combination of log-polar maps, and peak processing. The combination of maps is aimed at highlighting some peaks in the log-polar map using information from different bands. The method is robust and has been successfully tested for any rotation angle with commonly used hyperspectral scenes in remote sensing for scales of up to 7.5× and with pairs of hyperspectral images taken on different dates by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor for scales of up to 6.0×gl
dc.description.peerreviewedSIgl
dc.description.sponsorshipThis work was supported in part by the Consellería de Cultura, Educación e Ordenación Universitaria [grant numbers GRC2014/008 and ED431G/08] and Ministry of Education, Culture and Sport, Government of Spain [grant numbers TIN2013-41129-P and TIN2016-76373-P] both are co-funded by the European Regional Development Fund (ERDF)gl
dc.identifier.citationOrdóñez, Á., Argüello, F., & Heras, D. (2017). Fourier–Mellin registration of two hyperspectral images. International Journal Of Remote Sensing, 38(11), 3253-3273. doi: 10.1080/01431161.2017.1292071gl
dc.identifier.doi10.1080/01431161.2017.1292071
dc.identifier.essn1366-5901
dc.identifier.issn0143-1161
dc.identifier.urihttp://hdl.handle.net/10347/17879
dc.language.isoenggl
dc.publisherTaylor & Francisgl
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2013-41129-P/ES/SOLUCIONES HARDWARE Y SOFTWARE PARA LA COMPUTACION DE ALTAS PRESTACIONES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2016-76373-P/ES/
dc.relation.publisherversionhttps://doi.org/10.1080/01431161.2017.1292071gl
dc.rights© 2017 Taylor & Francis Group, LLCgl
dc.rights.accessRightsopen accessgl
dc.subjectHyperspectral datagl
dc.subjectImage registrationgl
dc.subjectFourier transformgl
dc.subjectFeature extractiongl
dc.subjectRemote sensinggl
dc.titleFourier–Mellin registration of two hyperspectral imagesgl
dc.typejournal articlegl
dc.type.hasVersionAMgl
dspace.entity.typePublication
relation.isAuthorOfPublicationa22a0ed8-b87b-473e-b16c-58d78c852dfd
relation.isAuthorOfPublication01d58a96-54b8-492d-986c-f9005bac259c
relation.isAuthorOfPublication24b7bf8f-61a5-44da-9a17-67fb85eab726
relation.isAuthorOfPublication.latestForDiscoverya22a0ed8-b87b-473e-b16c-58d78c852dfd

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