Remote sensing image interpretation with semantic graph-based methods: a survey

With the significant improvements in Earth observation (EO) technologies, remote sensing (RS) data exhibit the typical characteristics of Big Data. Propelled by the powerful feature extraction capabilities of intelligent algorithms, RS image interpretation has drawn remarkable attention and achieved...

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Detalles Bibliográficos
Autores: Sun, Shengtao, Dustdar, Schahram, Ranjan, Rajiv, Morgan, Graham, Dong, Yusen, Wang, Lizhe
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/57649
Acceso en línea:http://hdl.handle.net/10230/57649
http://dx.doi.org/10.1109/JSTARS.2022.3176612
Access Level:acceso abierto
Palabra clave:Remote sensing (RS) image interpretation
remotes sensing big data
RS geological interpretation
semantic graph-based method
semantic knowledge graph
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spelling Remote sensing image interpretation with semantic graph-based methods: a surveySun, ShengtaoDustdar, SchahramRanjan, RajivMorgan, GrahamDong, YusenWang, LizheRemote sensing (RS) image interpretationremotes sensing big dataRS geological interpretationsemantic graph-based methodsemantic knowledge graphWith the significant improvements in Earth observation (EO) technologies, remote sensing (RS) data exhibit the typical characteristics of Big Data. Propelled by the powerful feature extraction capabilities of intelligent algorithms, RS image interpretation has drawn remarkable attention and achieved progress. However, the semantic relationship and domain knowledge hidden in massive RS images have not been fully exploited. To the best of our knowledge, a comprehensive review of recent achievements regarding semantic graph-based methods for comprehension and interpretation of RS images is still lacking. Specifically, this article discusses the main challenges of RS image interpretation and presents a systematic survey of typical semantic graph-based methodologies for RS knowledge representation and understanding, including the Ontology Model, Geo-Information Tupu, and Semantic Knowledge Graph. Furthermore, we categorize and summarize how the existing technologies address different challenges in RS image interpretation based on semantic graph-based methods, which indicates that the semantic information about potential relationships and prior knowledge of variant RS targets are central to the solution. In addition, a case study of RS geological interpretation based on the semantic knowledge graph is demonstrated to show the promising capability of intelligent RS image interpretation. Finally, the future directions are discussed for further research.Institute of Electrical and Electronics Engineers (IEEE)202320232022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/57649http://dx.doi.org/10.1109/JSTARS.2022.3176612reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022;15:4544-58.This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/576492026-06-12T07:21:37Z
dc.title.none.fl_str_mv Remote sensing image interpretation with semantic graph-based methods: a survey
title Remote sensing image interpretation with semantic graph-based methods: a survey
spellingShingle Remote sensing image interpretation with semantic graph-based methods: a survey
Sun, Shengtao
Remote sensing (RS) image interpretation
remotes sensing big data
RS geological interpretation
semantic graph-based method
semantic knowledge graph
title_short Remote sensing image interpretation with semantic graph-based methods: a survey
title_full Remote sensing image interpretation with semantic graph-based methods: a survey
title_fullStr Remote sensing image interpretation with semantic graph-based methods: a survey
title_full_unstemmed Remote sensing image interpretation with semantic graph-based methods: a survey
title_sort Remote sensing image interpretation with semantic graph-based methods: a survey
dc.creator.none.fl_str_mv Sun, Shengtao
Dustdar, Schahram
Ranjan, Rajiv
Morgan, Graham
Dong, Yusen
Wang, Lizhe
author Sun, Shengtao
author_facet Sun, Shengtao
Dustdar, Schahram
Ranjan, Rajiv
Morgan, Graham
Dong, Yusen
Wang, Lizhe
author_role author
author2 Dustdar, Schahram
Ranjan, Rajiv
Morgan, Graham
Dong, Yusen
Wang, Lizhe
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Remote sensing (RS) image interpretation
remotes sensing big data
RS geological interpretation
semantic graph-based method
semantic knowledge graph
topic Remote sensing (RS) image interpretation
remotes sensing big data
RS geological interpretation
semantic graph-based method
semantic knowledge graph
description With the significant improvements in Earth observation (EO) technologies, remote sensing (RS) data exhibit the typical characteristics of Big Data. Propelled by the powerful feature extraction capabilities of intelligent algorithms, RS image interpretation has drawn remarkable attention and achieved progress. However, the semantic relationship and domain knowledge hidden in massive RS images have not been fully exploited. To the best of our knowledge, a comprehensive review of recent achievements regarding semantic graph-based methods for comprehension and interpretation of RS images is still lacking. Specifically, this article discusses the main challenges of RS image interpretation and presents a systematic survey of typical semantic graph-based methodologies for RS knowledge representation and understanding, including the Ontology Model, Geo-Information Tupu, and Semantic Knowledge Graph. Furthermore, we categorize and summarize how the existing technologies address different challenges in RS image interpretation based on semantic graph-based methods, which indicates that the semantic information about potential relationships and prior knowledge of variant RS targets are central to the solution. In addition, a case study of RS geological interpretation based on the semantic knowledge graph is demonstrated to show the promising capability of intelligent RS image interpretation. Finally, the future directions are discussed for further research.
publishDate 2022
dc.date.none.fl_str_mv 2022
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/57649
http://dx.doi.org/10.1109/JSTARS.2022.3176612
url http://hdl.handle.net/10230/57649
http://dx.doi.org/10.1109/JSTARS.2022.3176612
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022;15:4544-58.
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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