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Cloud Implementation of Extreme Learning Machine for Hyperspectral Image Classification

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dc.contributor.authorHaut, Juan M.
dc.contributor.authorMoreno Álvarez, Sergio
dc.contributor.authorMoreno Ávila, Enrique
dc.contributor.authorAyma Quirita, Victor Andrés
dc.contributor.authorPastor Vargas, Rafael
dc.contributor.authorPaoletti, Mercedes Eugenia
dc.contributor.orcidhttps://orcid.org/0000-0001-6701-961X
dc.contributor.orcidhttps://orcid.org/0000-0003-2987-2761
dc.contributor.orcidhttps://orcid.org/0000-0002-4089-9538
dc.contributor.orcidhttps://orcid.org/0000-0003-1030-3729
dc.date.accessioned2024-11-19T08:45:29Z
dc.date.available2024-11-19T08:45:29Z
dc.date.issued2023
dc.descriptionThe registered version of this article, first published in “IEEE Geoscience and Remote Sensing Letters, vol. 20, 2023", is available online at the publisher's website: IEEE, https://doi.org/10.1109/LGRS.2023.3295742 La versión registrada de este artículo, publicado por primera vez en “IEEE Geoscience and Remote Sensing Letters, vol. 20, 2023", está disponible en línea en el sitio web del editor: IEEE, https://doi.org/10.1109/LGRS.2023.3295742
dc.description.abstractClassifying remotely sensed hyperspectral images (HSIs) became a computationally demanding task given the extensive information contained throughout the spectral dimension. Furthermore, burgeoning data volumes compound inherent computational and storage challenges for data processing and classification purposes. Given their distributed processing capabilities, cloud environments have emerged as feasible solutions to handle these hurdles. This encourages the development of innovative distributed classification algorithms that take full advantage of the processing capabilities of such environments. Recently, computational-efficient methods have been implemented to boost network convergence by reducing the required training calculations. This letter develops a novel cloud-based distributed implementation of the extreme learning machine ( CC-ELM ) algorithm for efficient HSI classification. The proposal implements a fault-tolerant and scalable computing design while avoiding traditional batch-based backpropagation. CC-ELM has been evaluated over state-of-the-art HSI classification benchmarks, yielding promising results and proving the feasibility of cloud environments for large remote sensing and HSI data volumes processing. The code available at https://github.com/mhaut/scalable-ELM-HSIen
dc.description.versionversión publicada
dc.identifier.citationJ. M. Haut, S. Moreno-Álvarez, E. Moreno-Ávila, V. A. Ayma, R. Pastor-Vargas and M. E. Paoletti, "Cloud Implementation of Extreme Learning Machine for Hyperspectral Image Classification," in IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023, Art no. 5506905, doi: 10.1109/LGRS.2023.3295742
dc.identifier.doihttps://doi.org/10.1109/LGRS.2023.3295742
dc.identifier.issn1545-598X, eISSN: 1558-0571
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24421
dc.journal.titleIEEE Geoscience and Remote Sensing Letters
dc.journal.volume20
dc.language.isoen
dc.page.initial5506905
dc.publisherIEEE
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.relation.departmentSistemas de Comunicación y Control
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
dc.subject.keywordstrainingen
dc.subject.keywordshyperspectral imagingen
dc.subject.keywordscloud computingen
dc.subject.keywordsclassification algorithmsen
dc.subject.keywordsscalabilityen
dc.subject.keywordsproposalsen
dc.subject.keywordscluster computingen
dc.titleCloud Implementation of Extreme Learning Machine for Hyperspectral Image Classificationen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication3482d7bc-e120-48a3-812e-cc4b25a6d2fe
relation.isAuthorOfPublication.latestForDiscovery3482d7bc-e120-48a3-812e-cc4b25a6d2fe
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