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Hyperspectral Image Analysis Using Cloud-Based Support Vector Machines

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dc.contributor.authorHaut, Juan M.
dc.contributor.authorFranco Valiente, José M.
dc.contributor.authorPaoletti, Mercedes Eugenia
dc.contributor.authorMoreno Álvarez, Sergio
dc.contributor.authorPardo-Diaz, Alfonso
dc.contributor.orcidhttps://orcid.org/0000-0001-6701-961X
dc.contributor.orcidhttps://orcid.org/0000-0002-3880-6697
dc.contributor.orcidhttps://orcid.org/0000-0003-1030-3729
dc.date.accessioned2024-11-20T10:36:29Z
dc.date.available2024-11-20T10:36:29Z
dc.date.issued2024
dc.descriptionThe registered version of this article, first published in “SN Computer Science, 2024, vol. 5", is available online at the publisher's website: Springer, https://doi.org/10.1007/s42979-024-03073-z La versión registrada de este artículo, publicado por primera vez en “SN Computer Science, 2024, vol. 5", está disponible en línea en el sitio web del editor: Springer, https://doi.org/10.1007/s42979-024-03073-z
dc.description.abstractHyperspectral image processing techniques involve time-consuming calculations due to the large volume and complexity of the data. Indeed, hyperspectral scenes contain a wealth of spatial and spectral information thanks to the hundreds of narrow and continuous bands collected across the electromagnetic spectrum. Predictive models, particularly supervised machine learning classifiers, take advantage of this information to predict the pixel categories of images through a training set of real observations. Most notably, the Support Vector Machine (SVM) has demonstrate impressive accuracy results for image classification. Notwithstanding the performance offered by SVMs, dealing with such a large volume of data is computationally challenging. In this paper, a scalable and high-performance cloud-based approach for distributed training of SVM is proposed. The proposal address the overwhelming amount of remote sensing (RS) data information through a parallel training allocation. The implementation is performed over a memory-efficient Apache Spark distributed environment. Experiments are performed on a benchmark of real hyperspectral scenes to show the robustness of the proposal. Obtained results demonstrate efficient classification whilst optimising data processing in terms of training times.en
dc.description.versionversión publicada
dc.identifier.citationHaut, J.M., Franco-Valiente, J.M., Paoletti, M.E. et al. Hyperspectral Image Analysis Using Cloud-Based Support Vector Machines. SN COMPUT. SCI. 5, 719 (2024). https://doi.org/10.1007/s42979-024-03073-z
dc.identifier.doihttps://doi.org/10.1007/s42979-024-03073-z
dc.identifier.issn2661-8907
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24446
dc.journal.issue719
dc.journal.titleSN Computer Science
dc.journal.volume5
dc.language.isoen
dc.publisherSpringer
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
dc.subject.keywordsCloud computing (CC)en
dc.subject.keywordsSupport vector machines (SVMs)en
dc.subject.keywordsHyperspectral imaging (HSIs)en
dc.subject.keywordsMachine learning (ML)en
dc.subject.keywordsRemote sensing (RS)en
dc.titleHyperspectral Image Analysis Using Cloud-Based Support Vector Machinesen
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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