Publicación:
Cloud-Based Analysis of Large-Scale Hyperspectral Imagery for Oil Spill Detection

Vista sencilla de ítem
dc.contributor.authorHaut, Juan M.
dc.contributor.authorMoreno Álvarez, Sergio
dc.contributor.authorPastor Vargas, Rafael
dc.contributor.authorPérez García, Ámbar
dc.contributor.authorPaoletti, Mercedes Eugenia
dc.contributor.orcidhttps://orcid.org/0000-0001-6701-961X
dc.contributor.orcidhttps://orcid.org/0000-0002-4089-9538
dc.contributor.orcidhttps://orcid.org/0000-0002-2943-6348
dc.contributor.orcidhttps://orcid.org/0000-0003-1030-3729
dc.date.accessioned2024-11-20T08:31:26Z
dc.date.available2024-11-20T08:31:26Z
dc.date.issued2024
dc.descriptionThe registered version of this article, first published in “IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 2024", is available online at the publisher's website: IEEE, https://doi.org/10.1109/JSTARS.2023.3344022 La versión registrada de este artículo, publicado por primera vez en “IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 2024", está disponible en línea en el sitio web del editor: IEEE, https://doi.org/10.1109/JSTARS.2023.3344022
dc.description.abstractSpectral indices are of fundamental importance in providing insights into the distinctive characteristics of oil spills, making them indispensable tools for effective action planning. The normalized difference oil index (NDOI) is a reliable metric and suitable for the detection of coastal oil spills, effectively leveraging the visible and near-infrared (VNIR) spectral bands offered by commercial sensors. The present study explores the calculation of NDOI with a primary focus on leveraging remotely sensed imagery with rich spectral data. This undertaking necessitates a robust infrastructure to handle and process large datasets, thereby demanding significant memory resources and ensuring scalability. To overcome these challenges, a novel cloud-based approach is proposed in this study to conduct the distributed implementation of the NDOI calculation. This approach offers an accessible and intuitive solution, empowering developers to harness the benefits of cloud platforms. The evaluation of the proposal is conducted by assessing its performance using the scene acquired by the airborne visible infrared imaging spectrometer (AVIRIS) sensor during the 2010 oil rig disaster in the Gulf of Mexico. The catastrophic nature of the event and the subsequent challenges underscore the importance of remote sensing (RS) in facilitating decision-making processes. In this context, cloud-based approaches have emerged as a prominent technological advancement in the RS field. The experimental results demonstrate noteworthy performance by the proposed cloud-based approach and pave the path for future research for fast decision-making applications in scalable environments.en
dc.description.versionversión publicada
dc.identifier.citationJ. M. Haut, S. Moreno-Alvarez, R. Pastor-Vargas, A. Perez-Garcia and M. E. Paoletti, "Cloud-Based Analysis of Large-Scale Hyperspectral Imagery for Oil Spill Detection," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 2461-2474, 2024, doi: 10.1109/JSTARS.2023.3344022
dc.identifier.doihttps://doi.org/10.1109/JSTARS.2023.3344022
dc.identifier.issn2151-1535, 1939-1404
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24439
dc.journal.titleIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
dc.journal.volume17
dc.language.isoen
dc.page.final2474
dc.page.initial2461
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.keywordsOilsen
dc.subject.keywordsHyperspectral imagingen
dc.subject.keywordsIndexesen
dc.subject.keywordsCloud computingen
dc.subject.keywordsScalabilityen
dc.subject.keywordsEuropeen
dc.subject.keywordsMonitoringen
dc.titleCloud-Based Analysis of Large-Scale Hyperspectral Imagery for Oil Spill Detectionen
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_2023CloudBasedAnalysisOf.pdf
Tamaño:
2.43 MB
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