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Distributed Deep Learning for Remote Sensing Data Interpretation

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dc.contributor.authorHaut, Juan Mario
dc.contributor.authorPaoletti, Mercedes Eugenia
dc.contributor.authorMoreno Álvarez, Sergio
dc.contributor.authorPlaza, Javier
dc.contributor.authorRico Gallego, Juan Antonio
dc.contributor.authorPlaza, Antonio
dc.contributor.orcidhttps://orcid.org/0000-0001-6701-961X
dc.contributor.orcidhttps://orcid.org/0000-0003-1030-3729
dc.contributor.orcidhttps://orcid.org/0000-0002-2384-9141
dc.contributor.orcidhttps://orcid.org/0000-0002-4264-7473
dc.contributor.orcidhttps://orcid.org/0000-0002-9613-1659
dc.date.accessioned2024-11-15T12:51:01Z
dc.date.available2024-11-15T12:51:01Z
dc.date.issued2021-03-15
dc.descriptionThe registered version of this article, first published in “Proceesings of the IEEE, 109, 2021", is available online at the publisher's website: IEEE, https://doi.org/10.1109/JPROC.2021.3063258 La versión registrada de este artículo, publicado por primera vez en “Proceesings of the IEEE, 109, 2021", está disponible en línea en el sitio web del editor: IEEE, https://doi.org/10.1109/JPROC.2021.3063258
dc.description.abstractAs a newly emerging technology, deep learning (DL) is a very promising field in big data applications. Remote sensing often involves huge data volumes obtained daily by numerous in-orbit satellites. This makes it a perfect target area for data-driven applications. Nowadays, technological advances in terms of software and hardware have a noticeable impact on Earth observation applications, more specifically in remote sensing techniques and procedures, allowing for the acquisition of data sets with greater quality at higher acquisition ratios. This results in the collection of huge amounts of remotely sensed data, characterized by their large spatial resolution (in terms of the number of pixels per scene), and very high spectral dimensionality, with hundreds or even thousands of spectral bands. As a result, remote sensing instruments on spaceborne and airborne platforms are now generating data cubes with extremely high dimensionality, imposing several restrictions in terms of both processing runtimes and storage capacity. In this article, we provide a comprehensive review of the state of the art in DL for remote sensing data interpretation, analyzing the strengths and weaknesses of the most widely used techniques in the literature, as well as an exhaustive description of their parallel and distributed implementations (with a particular focus on those conducted using cloud computing systems). We also provide quantitative results, offering an assessment of a DL technique in a specific case study (source code available: https://github.com/mhaut/cloud-dnn-HSI). This article concludes with some remarks and hints about future challenges in the application of DL techniques to distributed remote sensing data interpretation problems. We emphasize the role of the cloud in providing a powerful architecture that is now able to manage vast amounts of remotely sensed data due to its implementation simplicity, low cost, and high efficiency compared to other parallel and distributed architectures, such as grid computing or dedicated clusters.en
dc.description.versionversión final
dc.identifier.citationJuan M Haut, Mercedes E Paoletti, Sergio Moreno-Álvarez, Javier Plaza, Juan-Antonio Rico-Gallego, Antonio Plaza. "Distributed deep learning for remote sensing data interpretation". Proceesings of the IEEE, 109, 8, 15 March 2021, 1320-1349. https://doi.org/10.1109/JPROC.2021.3063258
dc.identifier.doihttps://doi.org/10.1109/JPROC.2021.3063258
dc.identifier.issn0018-9219 eISSN: 1558-2256
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24390
dc.journal.issue8
dc.journal.titleProceesings of the IEEE
dc.journal.volume109
dc.language.isoen
dc.page.final1349
dc.page.initial1320
dc.publisherIEEE
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.keywordsBig dataen
dc.subject.keywordscloud computingen
dc.subject.keywordsdeep learning (DL)en
dc.subject.keywordsparallel and distributed architecturesen
dc.subject.keywordsremote sensingen
dc.titleDistributed Deep Learning for Remote Sensing Data Interpretationen
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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