Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/89085
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Building and Real Estate | - |
dc.contributor | Research Institute for Sustainable Urban Development | - |
dc.creator | Luo, TXH | - |
dc.creator | Lai, WWL | - |
dc.date.accessioned | 2021-02-04T02:39:13Z | - |
dc.date.available | 2021-02-04T02:39:13Z | - |
dc.identifier.issn | 1939-1404 | - |
dc.identifier.uri | http://hdl.handle.net/10397/89085 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ | en_US |
dc.rights | The following publication Luo, T. X. -., & Lai, W. W. L. (2020). Subsurface diagnosis with time-lapse GPR slices and change detection algorithms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 935-940 is available at https://dx.doi.org/10.1109/JSTARS.2020.2975659 | en_US |
dc.subject | Ground penetrating radar (Gpr) | en_US |
dc.subject | Subsurface diagnosis | en_US |
dc.subject | Temporal change detection | en_US |
dc.subject | Time-Lapse | en_US |
dc.title | Subsurface diagnosis with time-lapse GPR slices and change detection algorithms | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 935 | - |
dc.identifier.epage | 940 | - |
dc.identifier.volume | 13 | - |
dc.identifier.doi | 10.1109/JSTARS.2020.2975659 | - |
dcterms.abstract | This article explores the capability of applying time-lapse ground penetrating radar (GPR) data to investigate the health condition of an urban subsurface. A workflow is proposed to semi-automatically extract changes from time-lapse GPR C-scans. The developed workflow consists of two main steps, in which the first step is image registration and intensity normalization. The workflow uses benchmark points on the ground to normalize the global intensity of time-lapse GPR C-scans. The second step classifies pixels into change or unchanged group. Two kinds of information are considered to construct two difference-maps: changes in the image intensity and the object structure. K-means clustering is responsible for extracting pixels that possess both intensity changes and object structure changes - where potential subsurface defects most likely occurred. The workflow was verified by a site experiment, and the area of excavation with pipe replacement was successfully identified. The performance of the proposed workflow was promising in excluding small and random scattering noise, which was the main challenge in a time-lapse GPR survey. The article serves as a prototype and demonstrates the feasibility and necessity of conducting temporal diagnosis on the subsurface structure. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE journal of selected topics in applied earth observations and remote sensing, 28 Feb. 2020, v. 13, p. 935-940 | - |
dcterms.isPartOf | IEEE journal of selected topics in applied earth observations and remote sensing | - |
dcterms.issued | 2020-02 | - |
dc.identifier.scopus | 2-s2.0-85082382063 | - |
dc.identifier.eissn | 2151-1535 | - |
dc.description.validate | 202101 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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09018190.pdf | 1.73 MB | Adobe PDF | View/Open |
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