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dc.contributor.authorAwrangjeb, Mohammad
dc.date.accessioned2020-01-14T01:12:05Z
dc.date.available2020-01-14T01:12:05Z
dc.date.issued2019
dc.identifier.issn2072-4292en_US
dc.identifier.doi10.3390/rs11151798en_US
dc.identifier.urihttp://hdl.handle.net/10072/390200
dc.description.abstractHigh density airborne point cloud data have become an important means for modelling and maintenance of power line corridors (PLCs). As the amount of data in a dense point cloud is large, even in a small area, automatic detection of pylon locations can offer a significant advantage by reducing the number of points that need to be processed in subsequent steps, i.e., the extraction of individual pylons and wires. However, the existing solutions mostly overlook this advantage by processing all of the available data at one time, which hinders their application to large datasets. Moreover, the presence of high vegetation and hilly terrain may challenge many of the existing methods, since vertically overlapping objects (e.g., trees and wires) may not be effectively segmented using a single height threshold. For extraction of pylons and wires, this paper proposes a novel approach which involves converting the input points at different height levels into binary masks. Long straight lines are extracted from these masks and convex hulls around the lines at individual height levels are used to form series of hulls across the height levels. The series of hulls are then projected onto a horizontal plane to form individual corridors. A number of height gaps, where there are no objects between the vegetation and the bottom-most wire, are then estimated. The height gaps along with the height levels consider the presence of hilly terrain as well as high vegetation within the PLCs. By using only the non-ground points within the extracted corridors and height gaps, the pylons are detected. The estimated height gaps are further exploited to define robust seed regions for the detected pylons. The seed regions thereafter are grown to extract the complete pylons. Finally, only the points between the locations of two successive pylons are used to extract points of individual wires. It first counts the number of wires within a power line span and, then, iteratively obtains individual wire points. When tested on two large Australian datasets, the proposed approach exhibited high object-based performance (correctness for pylons and wires of 100% and 99.6%, respectively) and high point-based performance (completeness for pylons and wires of 98.1% and 95%, respectively). Moreover, the planimetric accuracy for the detected pylons was 0.10 m. Thus, the proposed approach is demonstrated to be useful in effective extraction and modelling of pylons and wires.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en_US
dc.relation.ispartofissue15en_US
dc.relation.ispartofjournalRemote Sensingen_US
dc.relation.ispartofvolume11en_US
dc.subject.fieldofresearchPhysical Geography and Environmental Geoscienceen_US
dc.subject.fieldofresearchGeomatic Engineeringen_US
dc.subject.fieldofresearchClassical Physicsen_US
dc.subject.fieldofresearchcode0406en_US
dc.subject.fieldofresearchcode0909en_US
dc.subject.fieldofresearchcode0203en_US
dc.subject.keywordsScience & Technologyen_US
dc.subject.keywordsTechnologyen_US
dc.subject.keywordsRemote Sensingen_US
dc.subject.keywordspower lineen_US
dc.subject.keywordscorridoren_US
dc.titleExtraction of Power Line Pylons and Wires Using Airborne LiDAR Data at Different Height Levelsen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationAwrangjeb, M, Extraction of Power Line Pylons and Wires Using Airborne LiDAR Data at Different Height Levels, Remote Sensing, 2019, 11 (15)en_US
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/en_US
dc.date.updated2020-01-14T01:09:31Z
dc.description.versionPublisheden_US
gro.rights.copyright© 2019 The Author(s). Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
gro.hasfulltextFull Text
gro.griffith.authorAwrangjeb, Mohammad


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