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dc.contributor.authorAwrangjeb, M
dc.contributor.authorIslam, MK
dc.date.accessioned2017-11-29T03:01:59Z
dc.date.available2017-11-29T03:01:59Z
dc.date.issued2017
dc.identifier.issn2194-9042
dc.identifier.doi10.5194/isprs-annals-IV-4-W4-81-2017
dc.identifier.urihttp://hdl.handle.net/10072/354642
dc.description.abstractHigh density airborne point cloud data has become an important means for modelling and maintenance of a power line corridor. Since, the amount of data in a dense point cloud is huge even in a small area, an automatic detection of pylons in the corridor can be a prerequisite for efficient and effective extraction of wires in a subsequent step. However, the existing solutions mostly overlook this important requirement by processing the whole data into one go, which nonetheless will hinder their applications to large areas. This paper presents a new pylon detection technique from point cloud data. First, the input point cloud is divided into ground and nonground points. The non-ground points within a specific low height region are used to generate a pylon mask, where pylons are found stand-alone, not connected with any wires. The candidate pylons are obtained using a connected component analysis in the mask, followed by a removal of trees by comparing area, shape and symmetry properties of trees and pylons. Finally, the parallelism property of wires with the line connecting pair of candidate pylons is exploited to remove trees that have the same area and shape properties as pylons. Experimental results show that the proposed technique provides a high pylon detection rate in terms of completeness (100 %) and correctness (100 %).
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInternational Society for Photogrammetry and Remote Sensing (ISPRS)
dc.publisher.placeGermany
dc.relation.ispartofconferencenameGeoAdvances 2017
dc.relation.ispartofconferencetitleISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
dc.relation.ispartofdatefrom2017-10-14
dc.relation.ispartofdateto2017-10-15
dc.relation.ispartoflocationSafranbolu, Karabuk, Turkey
dc.relation.ispartofpagefrom81
dc.relation.ispartofpageto87
dc.relation.ispartofissue4W4
dc.relation.ispartofvolume4
dc.subject.fieldofresearchImage Processing
dc.subject.fieldofresearchComputer Vision
dc.subject.fieldofresearchPhotogrammetry and Remote Sensing
dc.subject.fieldofresearchcode080106
dc.subject.fieldofresearchcode080104
dc.subject.fieldofresearchcode090905
dc.titleClassifier-Free Detection of Power Line Pylons from Point Cloud Data
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.description.versionVersion of Record (VoR)
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© The Author(s) 2017. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorAwrangjeb, Mohammad
gro.griffith.authorIslam, Mohammad Khairul K.


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