Multi-GPU registration of high-resolution multispectral images using HSI-KAZE in a cluster system

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dc.contributor.affiliationUniversidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías da Informaciónes_ES
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Electrónica e Computaciónes_ES
dc.contributor.authorOrdóñez Iglesias, Álvaro
dc.contributor.authorBlanco Heras, Dora
dc.contributor.authorArgüello Pedreira, Francisco
dc.date.accessioned2024-02-01T12:51:42Z
dc.date.available2024-02-01T12:51:42Z
dc.date.issued2022
dc.descriptionThis a post-print of the article “Multi-GPU registration of high-resolution multispectral images using HSI-KAZE in a cluster system” published in the Proceedings of IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium. The published article is available on https://doi.org/ 10.1109/IGARSS46834.2022.9884717es_ES
dc.description.abstractFeature-based registration methods have been demonstrated to be very effective to register multispectral images with large distortions or transformations despite the higher execution time that they require. In this paper, a first approach to a multi-node, multi-GPU implementation of the Hyperspectral KAZE (HSI-KAZE) method for co-registering bands and multispectral images is presented. Different multispectral datasets are distributed among the available nodes of a cluster using MPI and exploiting the parallel stream-based capabilities of the GPUs inside each node using CUDA.es_ES
dc.description.sponsorshipThis work was supported in part by Ministerio de Ciencia e Innovación, Government of Spain [grant number ID2019-104834GB-I00], and Consellería de Cultura, Educación e Universidade [grant numbers ED431C 2018/19, and accreditation 2019-2022 ED431G-2019/04]. This work was also supported by Junta de Castilla y León (PROPHET-2 Project) [grant number VA226P20]. All are co-funded by the European Regional Development Fund (ERDF).es_ES
dc.identifier.urihttp://hdl.handle.net/10347/32200
dc.language.isoenges_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104834GB-I00/ES/COMPUTACION DE ALTAS PRESTACIONES Y CLOUD PARA APLICACIONES DE ALTO INTERES/es_ES
dc.relation.projectIDED431Ces_ES
dc.relation.projectIDED431G-2019/04es_ES
dc.relation.projectIDVA226P20es_ES
dc.rights© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.es_ES
dc.rights.accessRightsopen accesses_ES
dc.subjectMPIes_ES
dc.subjectGPUes_ES
dc.subjectCUDAes_ES
dc.subjectMultispectrales_ES
dc.subjectImage registrationes_ES
dc.subject.classification330406 Arquitectura de ordenadoreses_ES
dc.titleMulti-GPU registration of high-resolution multispectral images using HSI-KAZE in a cluster systemes_ES
dc.typeconference outputes_ES
dspace.entity.typePublication
relation.isAuthorOfPublicationa22a0ed8-b87b-473e-b16c-58d78c852dfd
relation.isAuthorOfPublication24b7bf8f-61a5-44da-9a17-67fb85eab726
relation.isAuthorOfPublication01d58a96-54b8-492d-986c-f9005bac259c
relation.isAuthorOfPublication.latestForDiscoverya22a0ed8-b87b-473e-b16c-58d78c852dfd

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