Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/116903
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorHe, Y-
dc.creatorNiu, L-
dc.creatorZhang, G-
dc.creatorLi, J-
dc.creatorLiu, T-
dc.creatorLiu, J-
dc.creatorChen, B-
dc.date.accessioned2026-01-21T03:53:49Z-
dc.date.available2026-01-21T03:53:49Z-
dc.identifier.issn1939-1404-
dc.identifier.urihttp://hdl.handle.net/10397/116903-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2025 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.rightsThe following publication Y. He et al., "Detailed Health Monitoring of Large-Scale Urban Infrastructure by Combining Optical and SAR Images," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 21419-21429, 2025 is available at https://doi.org/10.1109/JSTARS.2025.3592033.en_US
dc.subjectChange detectionen_US
dc.subjectLarge-scale urban infrastructureen_US
dc.subjectSlow and severe deformationen_US
dc.subjectSmall baseline subset interferometry synthetic aperture radar (SBAS-InSAR)en_US
dc.titleDetailed health monitoring of large-scale urban infrastructure by combining optical and SAR imagesen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage21419-
dc.identifier.epage21429-
dc.identifier.volume18-
dc.identifier.doi10.1109/JSTARS.2025.3592033-
dcterms.abstractIn recent years, due to the influence of surface activities caused by natural factors and human activities, numerous infrastructures in urban areas have safety issues involving slow and severe deformation, necessitating detailed health monitoring and hazard identification. Although the interferometry synthetic aperture radar (InSAR) technology can achieve high-precision in slow deformation monitoring, it is difficult to capture the deformation signals of infrastructure caused by external forces or violent self-generated deformation due to loss of coherence. Besides, urban ground object changes can readily lead to deformation of urban infrastructure, as manifested in the deformation maps monitored by InSAR technology. Therefore, this article proposes an innovative method for investigating detailed urban infrastructure health monitoring by combining InSAR technology and change detection based on multitemporal remote sensing data. The study area comprises Guangzhou and Foshan in China experiencing significant urbanization. First, small baseline subset InSAR and independent component analysis are used to explain the spatio-temporal patterns of urban infrastructure with slow deformation in Guangzhou and Foshan. Subsequently, the ChangeClip model is employed to automatically detect drastic change activities of the infrastructure based on multi-temporal imagery. It is found that not a few infrastructures suffered sharp deformation such as road compression, building demolition and construction, and others. Finally, by overlapping the surface change detection map and urban infrastructure deformation map, specific causes and detailed health monitoring of urban infrastructure are identified. It is found that there are not a few buildings and subways with obvious slow and severe deformation behavior. Some infrastructures still have obvious deformation behavior in Guangzhou and Foshan, which requires further monitoring. All in all, by combining optical change detection and InSAR techniques, we could not only monitor slow and severe deformation for large-scale urban infrastructure, but also identify the deformation trigger factors and detailed hazards, and thus, provide valuable information for future urban development.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE journal of selected topics in applied earth observations and remote sensing, 2025, v. 18, p. 21419-21429-
dcterms.isPartOfIEEE journal of selected topics in applied earth observations and remote sensing-
dcterms.issued2025-
dc.identifier.scopus2-s2.0-105011938242-
dc.identifier.eissn2151-1535-
dc.description.validate202601 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis work was supported in part by the National Key Research and Development Program of China under Grant 2022YFF0503900, in part by the Natural Science Foundation of Guangdong Province under Grant 2022A1515010113, and in part by the Natural Science Foundation of Shenzhen under Grant GXWD20220811163556001.en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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