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Πεδίο DCΤιμήΓλώσσα
dc.contributor.authorAgrafiotis, Panagiotis-
dc.contributor.authorSkarlatos, Dimitrios-
dc.contributor.authorGeorgopoulos, Andreas-
dc.contributor.authorKarantzalos, Konstantinos-
dc.date.accessioned2020-07-23T11:19:55Z-
dc.date.available2020-07-23T11:19:55Z-
dc.date.issued2019-10-01-
dc.identifier.citationRemote Sensing, 2019, vol. 11, no. 19, articl. no. 2225en_US
dc.identifier.issn20724292-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/18561-
dc.description.abstractThe determination of accurate bathymetric information is a key element for near offshore activities; hydrological studies, such as coastal engineering applications, sedimentary processes, hydrographic surveying, archaeological mapping and biological research. Through structure from motion (SfM) and multi-view-stereo (MVS) techniques, aerial imagery can provide a low-cost alternative compared to bathymetric LiDAR (Light Detection and Ranging) surveys, as it offers additional important visual information and higher spatial resolution. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this article, in order to overcome the water refraction errors in a massive and accurate way, we employ machine learning tools, which are able to learn the systematic underestimation of the estimated depths. In particular, an SVR (support vector regression) model was developed, based on known depth observations from bathymetric LiDAR surveys, which is able to accurately recover bathymetry from point clouds derived from SfM-MVS procedures. Experimental results and validation were based on datasets derived from different test-sites, and demonstrated the high potential of our approach. Moreover, we exploited the fusion of LiDAR and image-based point clouds towards addressing challenges of both modalities in problematic areas.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofRemote Sensingen_US
dc.rights© by the authors.en_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectPoint clouden_US
dc.subjectBathymetryen_US
dc.subjectSVMen_US
dc.subjectMachine learningen_US
dc.subjectUAVen_US
dc.subjectAerial imageryen_US
dc.subjectSeabed mappingen_US
dc.subjectRefraction effecten_US
dc.subjectLiDARen_US
dc.subjectFusionen_US
dc.subjectData integrationen_US
dc.titleDepthLearn: Learning to correct the refraction on point clouds derived from aerial imagery for accurate dense shallow water bathymetry based on SVMs-fusion with LiDAR point cloudsen_US
dc.typeArticleen_US
dc.collaborationNational Technical University Of Athensen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryCivil Engineeringen_US
dc.journalsOpen Accessen_US
dc.countryGreeceen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.3390/rs11192225en_US
dc.identifier.scopus2-s2.0-85073429435-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85073429435-
dc.relation.issue19en_US
dc.relation.volume11en_US
cut.common.academicyear2019-2020en_US
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.journal.journalissn2072-4292-
crisitem.journal.publisherMDPI-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0003-4474-5007-
crisitem.author.orcid0000-0002-2732-4780-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
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