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dc.contributor.authorOrdóñez Iglesias, Álvaro
dc.contributor.authorBlanco Heras, Dora
dc.contributor.authorArgüello Pedreira, Francisco Santiago
dc.contributor.authorDemir, Begüm
dc.date.accessioned2021-09-30T11:29:59Z
dc.date.available2021-09-30T11:29:59Z
dc.date.issued2020
dc.identifier.citationThe Journal of Supercomputing (2020)76:9478–9492. DOI 10.1007/s11227-020-03214-0
dc.identifier.issn0920-8542
dc.identifier.urihttp://hdl.handle.net/10347/26958
dc.descriptionThis is a post-peer-review, pre-copyedit version of an article published in The Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-020-03214-0
dc.description.abstractImage registration is a common task in remote sensing, consisting in aligning different images of the same scene. It is a computationally expensive process, especially if high precision is required, the resolution is high, or consist of a large number of bands, as is the case of the hyperspectral images. HSIKAZEisaregistration method specially adapted for hyperspectral images that is based on feature detection and takes profit of the spatial and the spectral information available in those images. In this paper, an implementation of the HSI–KAZE registration algorithm on GPUs using CUDA is proposed. It detects keypoints based on non–linear diffusion filtering and is suitable for on–board processing of high resolution hyperspectral images. The algorithm includes a band selection method based on the entropy, construction of a scale-space through of non-linear filtering, keypoint detection with position refinement, and keypoint descriptors with spatial and spectral parts. Several techniques have been applied to obtain optimum performance on the GPU
dc.description.sponsorshipThis work was supported in part by the Consellería de Educación, Universidade e Formación Profesional [Grant Nos. GRC2014/008, ED431C 2018/19 and ED431G/08] and Ministerio de Economía y Empresa, Government of Spain [grant number TIN2016-76373-P] and by Junta de Castilla y Leon - ERDF (PROPHET Project) [Grant No. VA082P17]. All are cofunded by the European Regional Development Fund (ERDF). The work of Álvaro Ordóñez was also supported by Ministerio de Ciencia, Innovación y Universidades, Government of Spain, under a FPU Grant [Grant Nos. FPU16/03537 and EST18/00602]
dc.language.isoeng
dc.publisherSpringer
dc.rights© Springer Science+Business Media, LLC, part of Springer Nature 2020
dc.subjectHyperspectral data
dc.subjectImage registration
dc.subjectKAZE features
dc.subjectRemote sensing
dc.subjectCUDA
dc.subjectGPU
dc.titleGPU Accelerated Registration of Hyperspectral Images Using KAZE Features
dc.typejournal article
dc.identifier.doi10.1007/s11227-020-03214-0
dc.relation.publisherversionhttps://doi.org/10.1007/s11227-020-03214-0
dc.type.hasVersionAM
dc.identifier.essn1573-0484
dc.rights.accessRightsopen access
dc.contributor.affiliationUniversidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías da Información
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Electrónica e Computación
dc.description.peerreviewedSI
dc.relation.projectIDinfo:eu-repo/grantAgreement/MECD/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/FPU16%2F03537/ES
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.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/EST18%2F00602/ES


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