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

Spectral 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...

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Detalhes bibliográficos
Autores: Haut, Juan M., Moreno Álvarez, Sergio, Pastor Vargas, Rafael, Pérez García, Ámbar, Paoletti, Mercedes Eugenia
Tipo de documento: artigo
Data de publicação:2024
País:España
Recursos:Universidad Nacional de Educación a Distancia
Repositório:e-spacio. Repositorio Institucional de la UNED
Idioma:inglês
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/24439
Acesso em linha:https://hdl.handle.net/20.500.14468/24439
Access Level:Acceso aberto
Palavra-chave:12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
Oils
Hyperspectral imaging
Indexes
Cloud computing
Scalability
Europe
Monitoring
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spelling Cloud-Based Analysis of Large-Scale Hyperspectral Imagery for Oil Spill DetectionHaut, Juan M.Moreno Álvarez, SergioPastor Vargas, RafaelPérez García, ÁmbarPaoletti, Mercedes Eugenia12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 InformáticaOilsHyperspectral imagingIndexesCloud computingScalabilityEuropeMonitoringSpectral 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.IEEEhttps://orcid.org/0000-0001-6701-961Xhttps://orcid.org/0000-0002-4089-9538https://orcid.org/0000-0002-2943-6348https://orcid.org/0000-0003-1030-3729e-Spacio UNED20242024-11-2020242024-01-0120242024-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14468/24439reponame:e-spacio. Repositorio Institucional de la UNEDinstname:Universidad Nacional de Educación a DistanciaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.esoai:e-spacio.uned.es:20.500.14468/244392026-06-06T12:38:31Z
dc.title.none.fl_str_mv Cloud-Based Analysis of Large-Scale Hyperspectral Imagery for Oil Spill Detection
title Cloud-Based Analysis of Large-Scale Hyperspectral Imagery for Oil Spill Detection
spellingShingle Cloud-Based Analysis of Large-Scale Hyperspectral Imagery for Oil Spill Detection
Haut, Juan M.
12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
Oils
Hyperspectral imaging
Indexes
Cloud computing
Scalability
Europe
Monitoring
title_short Cloud-Based Analysis of Large-Scale Hyperspectral Imagery for Oil Spill Detection
title_full Cloud-Based Analysis of Large-Scale Hyperspectral Imagery for Oil Spill Detection
title_fullStr Cloud-Based Analysis of Large-Scale Hyperspectral Imagery for Oil Spill Detection
title_full_unstemmed Cloud-Based Analysis of Large-Scale Hyperspectral Imagery for Oil Spill Detection
title_sort Cloud-Based Analysis of Large-Scale Hyperspectral Imagery for Oil Spill Detection
dc.creator.none.fl_str_mv Haut, Juan M.
Moreno Álvarez, Sergio
Pastor Vargas, Rafael
Pérez García, Ámbar
Paoletti, Mercedes Eugenia
author Haut, Juan M.
author_facet Haut, Juan M.
Moreno Álvarez, Sergio
Pastor Vargas, Rafael
Pérez García, Ámbar
Paoletti, Mercedes Eugenia
author_role author
author2 Moreno Álvarez, Sergio
Pastor Vargas, Rafael
Pérez García, Ámbar
Paoletti, Mercedes Eugenia
author2_role author
author
author
author
dc.contributor.none.fl_str_mv https://orcid.org/0000-0001-6701-961X
https://orcid.org/0000-0002-4089-9538
https://orcid.org/0000-0002-2943-6348
https://orcid.org/0000-0003-1030-3729
e-Spacio UNED
dc.subject.none.fl_str_mv 12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
Oils
Hyperspectral imaging
Indexes
Cloud computing
Scalability
Europe
Monitoring
topic 12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
Oils
Hyperspectral imaging
Indexes
Cloud computing
Scalability
Europe
Monitoring
description Spectral 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.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-11-20
2024
2024-01-01
2024
2024-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14468/24439
url https://hdl.handle.net/20.500.14468/24439
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:e-spacio. Repositorio Institucional de la UNED
instname:Universidad Nacional de Educación a Distancia
instname_str Universidad Nacional de Educación a Distancia
reponame_str e-spacio. Repositorio Institucional de la UNED
collection e-spacio. Repositorio Institucional de la UNED
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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