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dc.contributor.author | Ordóñez Iglesias, Álvaro |
dc.contributor.author | Blanco Heras, Dora |
dc.contributor.author | Argüello Pedreira, Francisco Santiago |
dc.contributor.author | Demir, Begüm |
dc.date.accessioned | 2021-09-30T11:29:59Z |
dc.date.available | 2021-09-30T11:29:59Z |
dc.date.issued | 2020 |
dc.identifier.citation | The Journal of Supercomputing (2020)76:9478–9492. DOI 10.1007/s11227-020-03214-0 |
dc.identifier.issn | 0920-8542 |
dc.identifier.uri | http://hdl.handle.net/10347/26958 |
dc.description | This 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.abstract | Image 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.sponsorship | This 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.iso | eng |
dc.publisher | Springer |
dc.rights | © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
dc.subject | Hyperspectral data |
dc.subject | Image registration |
dc.subject | KAZE features |
dc.subject | Remote sensing |
dc.subject | CUDA |
dc.subject | GPU |
dc.title | GPU Accelerated Registration of Hyperspectral Images Using KAZE Features |
dc.type | journal article |
dc.identifier.doi | 10.1007/s11227-020-03214-0 |
dc.relation.publisherversion | https://doi.org/10.1007/s11227-020-03214-0 |
dc.type.hasVersion | AM |
dc.identifier.essn | 1573-0484 |
dc.rights.accessRights | open access |
dc.contributor.affiliation | Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías da Información |
dc.contributor.affiliation | Universidade de Santiago de Compostela. Departamento de Electrónica e Computación |
dc.description.peerreviewed | SI |
dc.relation.projectID | info:eu-repo/grantAgreement/MECD/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/FPU16%2F03537/ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2016-76373-P/ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/EST18%2F00602/ES |
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