Publicación:
Remote Sensing Image Classification Using CNNs With Balanced Gradient for Distributed Heterogeneous Computing

Vista sencilla de ítem
dc.contributor.authorMoreno Álvarez, Sergio
dc.contributor.authorPaoletti, Mercedes Eugenia
dc.contributor.authorCavallaro, Gabriele
dc.contributor.authorRico Gallego, Juan Antonio
dc.contributor.authorHaut, Juan M.
dc.contributor.orcidhttps://orcid.org/0000-0003-1030-3729
dc.contributor.orcidhttps://orcid.org/0000-0002-3239-9904
dc.contributor.orcidhttps://orcid.org/0000-0002-4264-7473
dc.contributor.orcidhttps://orcid.org/0000-0001-6701-961X
dc.date.accessioned2024-11-18T10:55:34Z
dc.date.available2024-11-18T10:55:34Z
dc.date.issued2022
dc.descriptionThe registered version of this article, first published in “IEEE Geoscience and Remote Sensing Letters, vol. 19, 2022", is available online at the publisher's website: IEEE, https://doi.org/10.1109/LGRS.2022.3173052 La versión registrada de este artículo, publicado por primera vez en “IEEE Geoscience and Remote Sensing Letters, vol. 19, 2022", está disponible en línea en el sitio web del editor: IEEE, https://doi.org/10.1109/LGRS.2022.3173052
dc.description.abstractLand-cover classification methods are based on the processing of large image volumes to accurately extract representative features. Particularly, convolutional models provide notable characterization properties for image classification tasks. Distributed learning mechanisms on high-performance computing platforms have been proposed to speed up the processing, while achieving an efficient feature extraction. High-performance computing platforms are commonly composed of a combination of central processing units (CPUs) and graphics processing units (GPUs) with different computational capabilities. As a result, current homogeneous workload distribution techniques for deep learning (DL) become obsolete due to their inefficient use of computational resources. To address this, new computational balancing proposals, such as heterogeneous data parallelism, have been implemented. Nevertheless, these techniques should be improved to handle the peculiarities of working with heterogeneous data workloads in the training of distributed DL models. The objective of handling heterogeneous workloads for current platforms motivates the development of this work. This letter proposes an innovative heterogeneous gradient calculation applied to land-cover classification tasks through convolutional models, considering the data amount assigned to each device in the platform while maintaining the acceleration. Extensive experimentation has been conducted on multiple datasets, considering different deep models on heterogeneous platforms to demonstrate the performance of the proposed methodology.en
dc.description.versionversión publicada
dc.identifier.citationS. Moreno-Álvarez, M. E. Paoletti, G. Cavallaro, J. A. Rico and J. M. Haut, "Remote Sensing Image Classification Using CNNs With Balanced Gradient for Distributed Heterogeneous Computing," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 3512205, doi: 10.1109/LGRS.2022.3173052
dc.identifier.doihttps://doi.org/10.1109/LGRS.2022.3173052
dc.identifier.issn1545-598X, eISSN: 1558-0571
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24402
dc.journal.titleIEEE Geoscience and Remote Sensing Letters
dc.journal.volume19
dc.language.isoes
dc.publisherIEEE
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/restrictedAccess
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.keywordsdistributed computingen
dc.subject.keywordsheterogeneous computingen
dc.subject.keywordsimage classificationen
dc.subject.keywordsremote sensingen
dc.titleRemote Sensing Image Classification Using CNNs With Balanced Gradient for Distributed Heterogeneous Computingen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication3482d7bc-e120-48a3-812e-cc4b25a6d2fe
relation.isAuthorOfPublication.latestForDiscovery3482d7bc-e120-48a3-812e-cc4b25a6d2fe
Archivos
Bloque original
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
MorenoAlvarez_Sergio_2022RemoteSensingImage.pdf
Tamaño:
839.75 KB
Formato:
Adobe Portable Document Format
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
3.62 KB
Formato:
Item-specific license agreed to upon submission
Descripción:
Descargar
Colecciones
Artículos y papers