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Deep mixed precision for hyperspectral image classification

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dc.contributor.authorPaoletti, Mercedes Eugenia
dc.contributor.authorX. Tao
dc.contributor.authorHaut, Juan Mario
dc.contributor.authorMoreno Álvarez, Sergio
dc.contributor.authorPlaza, Antonio
dc.contributor.orcidhttps://orcid.org/0000-0003-1030-3729
dc.contributor.orcidhttps://orcid.org/0000-0001-6701-961X
dc.contributor.orcidhttps://orcid.org/0000-0002-9613-1659
dc.date.accessioned2024-11-15T11:31:39Z
dc.date.available2024-11-15T11:31:39Z
dc.date.issued2021-02-03
dc.descriptionThe registered version of this article, first published in “Journal of Supercomputing 77, 2021", is available online at the publisher's website: Springer, https://doi.org/10.1007/s11227-021-03638-2 La versión registrada de este artículo, publicado por primera vez en “Journal of Supercomputing 77, 2021", está disponible en línea en el sitio web del editor: Springer, https://doi.org/10.1007/s11227-021-03638-2
dc.description.abstractHyperspectral images (HSIs) record scenes at different wavelength channels, providing detailed spatial and spectral information. How to storage and process this highdimensional data plays a vital role in many practical applications, where classification technologies have emerged as excellent processing tools. However, their high computational complexity and energy requirements bring some challenges. Adopting low-power consumption architectures and deep learning (DL) approaches has to provide acceptable computing capabilities without reducing accuracy demand. However, most DL architectures employ single-precision (FP32) to train models, and some big DL architectures will have a limitation on memory and computation resources. This can negatively affect the network learning process. This letter leads these challenges by using mixed precision into DL architectures for HSI classification to speed up the training process and reduce the memory consumption/access. Proposed models are evaluated on four widely used data sets. Also, low and highpower consumption devices are compared, considering NVIDIA Jetson Xavier and Titan RTX GPUs, to evaluate the proposal viability in on-board processing devices. Obtained results demonstrate the efficiency and effectiveness of these models within HSI classification task for both devices. Source codes: https ://githu b.com/mhaut / CNN-MP-HSI.en
dc.description.versionversión final
dc.identifier.citationPaoletti, M.E., Tao, X., Haut, J.M. et al. Deep mixed precision for hyperspectral image classification. J Supercomput 77, 9190–9201 (2021). https://doi.org/10.1007/s11227-021-03638-2
dc.identifier.doihttps://doi.org/10.1007/s11227-021-03638-2
dc.identifier.issn1573-0484, 0920-8542
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24389
dc.journal.issue8
dc.journal.titleJournal of Supercomputing
dc.journal.volume77
dc.language.isoen
dc.page.final9201
dc.page.initial9190
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.keywordshyperspectral imageen
dc.subject.keywordsdeeplearningen
dc.subject.keywordsmixed precisionen
dc.titleDeep mixed precision 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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