Point cloud filtering on UAV based point cloud

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dc.contributor.authorZeybek, Mustafa.
dc.contributor.authorŞanlıoğlu, İsmail.
dc.date.accessioned2020-03-26T20:19:09Z
dc.date.available2020-03-26T20:19:09Z
dc.date.issued2019
dc.departmentSelçuk Üniversitesi, Mühendislik Fakültesi, Harita Mühendisliği Bölümüen_US
dc.description.abstractNowadays, Unmanned Aerial Vehicles (UAVs) have been attracted wide attentions such as a new measurement equipment and mapping, which are capable of the high-resolution point cloud data collection. In addition, a massive point cloud data has brought about the data filtering and irregular data organization for the generation of digital terrain models. Filtering of point clouds contains vegetations and artificial objects play a crucial role for bare earth terrain modelling. Topographical maps rely on the data structures which are built on bare ground terrain points. The bare earth surface extraction is not the only crucial to the topographical maps but also decision-making processes such as natural hazards management, deformation analysis and interpretation. In order to filter a UAV-based 3D raw point cloud data, in this paper, filtering performance of four different algorithms using open source and commercial software's have been investigated, (1) curvature based (Multiscale Curvature Classification-MCC), (2) surface-based filtering (FUSION), (3) progressive TIN based (LasTool-LasGround module-commercial) and (4) physical simulation processing (Cloth Simulation Filtering-CSF). The applied filtering results were validated with the reference data set classified by operator. Although different filtering methodologies implemented on point clouds, these methods demonstrated similar results to extract ground on distinctive terrain feature such as dense vegetated, flat surface, rough and complex landscapes. The filtering algorithms' results revealed that UAV-generated data suitable for extraction of bare earth surface feature on the different type of a terrain. Accuracy of the filtered point cloud reached the 93% true classification on flat surfaces from CSF filtering method. (C) 2018 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipSelcuk University Scientific Research Projects Coordination Unit (BAP) [15401017, 2014-OYP-055]en_US
dc.description.sponsorshipThis paper is a part of Mustafa Zeybek's PhD thesis. This work was partly supported by the Selcuk University Scientific Research Projects Coordination Unit (BAP Grant No. 15401017 and 2014-OYP-055).en_US
dc.identifier.citationZeybek, M., Şanlıoğlu, İ. (2019). Point Cloud Filtering on UAV Based Point Cloud. Measurement, 133, 99-111.
dc.identifier.doi10.1016/j.measurement.2018.10.013en_US
dc.identifier.endpage111en_US
dc.identifier.issn0263-2241en_US
dc.identifier.issn1873-412Xen_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage99en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.measurement.2018.10.013
dc.identifier.urihttps://hdl.handle.net/20.500.12395/38112
dc.identifier.volume133en_US
dc.identifier.wosWOS:000449097800013en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorŞanlıoğlu, İsmail.
dc.language.isoenen_US
dc.publisherELSEVIER SCI LTDen_US
dc.relation.ispartofMEASUREMENTen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectUAVen_US
dc.subjectPoint clouden_US
dc.subjectFilteringen_US
dc.subjectBare earthen_US
dc.subjectExtractionen_US
dc.titlePoint cloud filtering on UAV based point clouden_US
dc.typeArticleen_US

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