Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/100709
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dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.creatorLuo, TXHen_US
dc.creatorLai, WWLen_US
dc.creatorChang, RKWen_US
dc.creatorGoodman, Den_US
dc.date.accessioned2023-08-11T03:12:50Z-
dc.date.available2023-08-11T03:12:50Z-
dc.identifier.issn0926-9851en_US
dc.identifier.urihttp://hdl.handle.net/10397/100709-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2019 Elsevier B.V. All rights reserved.en_US
dc.rights© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.rightsThe following publication Luo, T. X. H., Lai, W. W. L., Chang, R. K. W., & Goodman, D. (2019). GPR imaging criteria. Journal of Applied Geophysics, 165, 37-48 is available at https://doi.org/10.1016/j.jappgeo.2019.04.008.en_US
dc.subject3D imagingen_US
dc.subjectGPRen_US
dc.subjectImaging parametersen_US
dc.subjectStandardized workflowen_US
dc.titleGPR imaging criteriaen_US
dc.typeJournal/Magazine Articleen_US
dc.description.otherinformationTitle on author’s file: An Empirical Study of GPR 3D Imaging Criteriaen_US
dc.identifier.spage37en_US
dc.identifier.epage48en_US
dc.identifier.volume165en_US
dc.identifier.doi10.1016/j.jappgeo.2019.04.008en_US
dcterms.abstractGPR has been widely acknowledged as an effective and efficient technique for imaging the subsurface. But the process of constructing 3D GPR images (C-scans) is still subjective and mainly relies upon the operator's knowledge and experience. This study reviews the parameters that affect GPR imaging quality: namely, profile spacing (PS), slice thickness (ST) and interpolations. Feature characteristics that have a crucial influence on imaging quality were also identified. Through conducting 25 carefully designed empirical experiments on concrete as well as subsurface structures, the relationship between 3D imaging parameters and feature characteristics were observed. A general workflow was derived for GPR C-scan generation, which is analogous to the typical signal processing steps used in 2D radargram signal processing (Jol, 2009). Empirical values in workflow were based on the retrieval of known ground-truth data and comparison with the processed images, i.e. the closest to reality. Unlike 2D processing, the workflow for 3D is feature-oriented and case-specific, and the proposed workflow gives guidelines on suitable ranges for 3 major parameters when used in a variety of applications. It was identified that feature shapes and the ratios of feature size to radar footprint are of vital importance. With the proposed flowchart, the often vague “survey experience” is parametrized and standardized, and the upper and lower limits governing the generation of objective and trustworthy 3D GPR images are defined. This workflow for GPR 3D slice imaging also paves the way for GPR feature extraction and change detection commonly adopted in remote sensing.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of applied geophysics, June 2019, v. 165, p. 37-48en_US
dcterms.isPartOfJournal of applied geophysicsen_US
dcterms.issued2019-06-
dc.identifier.scopus2-s2.0-85064189004-
dc.description.validate202305 bckwen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberLSGI-0202-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS29143399-
dc.description.oaCategoryGreen (AAM)en_US
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