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

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• dc.contributor.author Sun, Shengtao
• dc.contributor.author Dustdar, Schahram
• dc.contributor.author Ranjan, Rajiv
• dc.contributor.author Morgan, Graham
• dc.contributor.author Dong, Yusen
• dc.contributor.author Wang, Lizhe
• dc.date.accessioned 2023-07-25T07:45:17Z
• dc.date.available 2023-07-25T07:45:17Z
• dc.date.issued 2022
• dc.description.abstract 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.
• dc.format.mimetype application/pdf
• dc.identifier.citation Sun S, Dustdar S, Ranjan R, Morgan G, Dong Y, Wang L. Remote sensing image interpretation with semantic graph-based methods: a survey. IEEE J Sel Top Appl Earth Obs Remote Sens. 2022;15:4544-58. DOI: 10.1109/JSTARS.2022.3176612
• dc.identifier.doi http://dx.doi.org/10.1109/JSTARS.2022.3176612
• dc.identifier.issn 1939-1404
• dc.identifier.uri http://hdl.handle.net/10230/57649
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022;15:4544-58.
• dc.rights This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Remote sensing (RS) image interpretation
• dc.subject.keyword remotes sensing big data
• dc.subject.keyword RS geological interpretation
• dc.subject.keyword semantic graph-based method
• dc.subject.keyword semantic knowledge graph
• dc.title Remote sensing image interpretation with semantic graph-based methods: a survey
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion