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Vertical optical scanning with panoramic vision for tree trunk reconstruction

dc.contributor.authorBerveglieri, Adilson [UNESP]
dc.contributor.authorTommaselli, Antonio M. G. [UNESP]
dc.contributor.authorLiang, Xinlian
dc.contributor.authorHonkavaara, Eija
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionNational Land Survey of Finland
dc.contributor.institutionAcademy of Finland
dc.date.accessioned2018-12-11T17:16:37Z
dc.date.available2018-12-11T17:16:37Z
dc.date.issued2017-12-02
dc.description.abstractThis paper presents a practical application of a technique that uses a vertical optical flow with a fisheye camera to generate dense point clouds from a single planimetric station. Accurate data can be extracted to enable the measurement of tree trunks or branches. The images that are collected with this technique can be oriented in photogrammetric software (using fisheye models) and used to generate dense point clouds, provided that some constraints on the camera positions are adopted. A set of images was captured in a forest plot in the experiments. Weighted geometric constraints were imposed in the photogrammetric software to calculate the image orientation, perform dense image matching, and accurately generate a 3D point cloud. The tree trunks in the scenes were reconstructed and mapped in a local reference system. The accuracy assessment was based on differences between measured and estimated trunk diameters at different heights. Trunk sections from an image-based point cloud were also compared to the corresponding sections that were extracted from a dense terrestrial laser scanning (TLS) point cloud. Cylindrical fitting of the trunk sections allowed the assessment of the accuracies of the trunk geometric shapes in both clouds. The average difference between the cylinders that were fitted to the photogrammetric cloud and those to the TLS cloud was less than 1 cm, which indicates the potential of the proposed technique. The point densities that were obtained with vertical optical scanning were 1/3 less than those that were obtained with TLS. However, the point density can be improved by using higher resolution cameras.en
dc.description.affiliationDepartment of Cartography São Paulo State University UNESP, 305
dc.description.affiliationDepartment of Remote Sensing and Photogrammetry Finnish Geodetic Institute FGI National Land Survey of Finland
dc.description.affiliationCentre of Excellence in Laser Scanning Research Academy of Finland
dc.description.affiliationUnespDepartment of Cartography São Paulo State University UNESP, 305
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 2013/50426-4
dc.description.sponsorshipIdFAPESP: 2014/05033-7
dc.identifierhttp://dx.doi.org/10.3390/s17122791
dc.identifier.citationSensors (Switzerland), v. 17, n. 12, 2017.
dc.identifier.doi10.3390/s17122791
dc.identifier.file2-s2.0-85036668592.pdf
dc.identifier.issn1424-8220
dc.identifier.scopus2-s2.0-85036668592
dc.identifier.urihttp://hdl.handle.net/11449/175595
dc.language.isoeng
dc.relation.ispartofSensors (Switzerland)
dc.relation.ispartofsjr0,584
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subject3D point cloud
dc.subjectDBH
dc.subjectDense image matching
dc.subjectDiameter at breast height
dc.subjectFisheye camera
dc.subjectPhotogrammetry
dc.subjectStructure from motion
dc.subjectTree trunk
dc.titleVertical optical scanning with panoramic vision for tree trunk reconstructionen
dc.typeArtigo
dspace.entity.typePublication
unesp.author.lattes5493428631948910[2]
unesp.author.orcid0000-0003-0483-1103[2]
unesp.departmentCartografia - FCTpt

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