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Multi-Frequency GPR Data Fusion with Genetic Algorithms for Archaeological Prospection

dc.contributor.authorWenke Zhao
dc.contributor.authorLin Yuan
dc.contributor.authorEmanuele Forte
dc.contributor.authorGuoze Lu
dc.contributor.authorGang Tian
dc.contributor.authorMichele Pipan
dc.contributor.otherSchool of Earth Sciences, Zhejiang University, Hangzhou 310058, China
dc.contributor.otherSchool of Earth Sciences, Zhejiang University, Hangzhou 310058, China
dc.contributor.otherDepartment of Mathematics and Geosciences, University of Trieste, 34128 Trieste, Italy
dc.contributor.otherSchool of Earth Sciences, Zhejiang University, Hangzhou 310058, China
dc.contributor.otherSchool of Earth Sciences, Zhejiang University, Hangzhou 310058, China
dc.contributor.otherDepartment of Mathematics and Geosciences, University of Trieste, 34128 Trieste, Italy
dc.date.accessioned2025-10-09T04:54:44Z
dc.date.available2025-10-09T04:54:44Z
dc.date.issued01-07-2021
dc.identifier.urihttps://www.mdpi.com/2072-4292/13/14/2804
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/40833
dc.description.abstractArchaeological GPR data from antennas of different frequencies allow the identification of buried cultural heritage at different scales. Therefore, multi-frequency GPR systems are recommended for complicated subsurface archaeological conditions. GPR data fusion approaches, automatically or semi-automatically, can integrate data measurements from different frequency antennas, combine them into a single representation, and partially overcome the unavoidable trade-off between penetration and resolution. We propose an adaptively weighted fusion method for multi-frequency GPR data based on genetic algorithms (GAs). In order to evaluate the feasibility and the effectiveness of the strategy for archaeological prospection, we tested the procedure on GPR datasets acquired in two totally different archaeological conditions: rammed layers of an ancient wall, in Henan Province, China, and complex and elusive prehistoric archaeological features within a natural stratigraphic sequence on the volcanic Stromboli Island, Italy. The results demonstrate that the proposed strategy can maximize the information content of GPR profiles, enhancing the GPR interpretation possibilities in an automatic and objective way for different targets and in different subsurface conditions.
dc.language.isoEN
dc.publisherMDPI AG
dc.subject.lccScience
dc.titleMulti-Frequency GPR Data Fusion with Genetic Algorithms for Archaeological Prospection
dc.typeArticle
dc.description.keywordsmulti-frequency GPR data fusion
dc.description.keywordsarchaeological prospection
dc.description.keywordsgenetic algorithms
dc.description.doi10.3390/rs13142804
dc.title.journalRemote Sensing
dc.identifier.e-issn2072-4292
dc.identifier.oaioai:doaj.org/journal:d8c5d505665e4336bc69f726ebc45097
dc.journal.infoVolume 13, Issue 14


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