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dc.contributor.authorNurunnabi, Abdul
dc.contributor.authorWest, Geoff
dc.contributor.authorBelton, David
dc.contributor.editorM. Scaioni
dc.contributor.editorR. C. Lindenbergh
dc.contributor.editorS. Oude Elberink
dc.contributor.editorD. Schneider
dc.contributor.editorF. Pirotti
dc.date.accessioned2017-01-30T12:52:19Z
dc.date.available2017-01-30T12:52:19Z
dc.date.created2014-03-20T20:00:39Z
dc.date.issued2013
dc.identifier.citationNurunnabi, Abdul and West, Geoff and Belton, David. 2013. Robust locally weighted regression for ground surface extraction in mobile laser scanning 3D data, in Scaioni, M. and Lindenbergh, R.C. and Oude Elberink, S. Schneider, D. and Pirotti, F. (ed), International Society for Photogrammetry and Remote Sensing (ISPRS) Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences, Nov 11-13 2013, II-5/W2: pp. 217-222. Antalya, Turkey: ISPRS. Antalya, Turkey: ISPRS.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/26232
dc.identifier.doi10.5194/isprsannals-II-5-W2-217-2013
dc.description.abstract

A new robust way for ground surface extraction from mobile laser scanning 3D point cloud data is proposed in this paper. Fitting polynomials along 2D/3D points is one of the well-known methods for filtering ground points, but it is evident that unorganized point clouds consist of multiple complex structures by nature so it is not suitable for fitting a parametric global model. The aim of this research is to develop and implement an algorithm to classify ground and non-ground points based on statistically robust locally weighted regression which fits a regression surface (line in 2D) by fitting without any predefined global functional relation among the variables of interest. Afterwards, the z (elevation)-values are robustly down weighted based on the residuals for the fitted points. The new set of down weighted z-values along with x (or y) values are used to get a new fit of the (lower) surface (line). The process of fitting and down-weighting continues until the difference between two consecutive fits is insignificant. Then the final fit represents the ground level of the given point cloud and the ground surface points can be extracted. The performance of the new method has been demonstrated through vehicle based mobile laser scanning 3D point cloud data from urban areas which include different problematic objects such as short walls, large buildings, electric poles, sign posts and cars. The method has potential in areas like building/construction footprint determination, 3D city modelling, corridor mapping and asset management.

dc.publisherISPRS
dc.subjectTerrain Classification
dc.subjectDTM
dc.subjectPoint Cloud
dc.subjectFiltering
dc.subject3D Modelling
dc.subjectSegmentation
dc.subjectFeature Extraction
dc.subjectOutlier
dc.titleRobust locally weighted regression for ground surface extraction in mobile laser scanning 3D data
dc.typeConference Paper
dcterms.source.startPage217
dcterms.source.endPage222
dcterms.source.issn2194-9042
dcterms.source.titleISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2, 2013
dcterms.source.seriesISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2, 2013
dcterms.source.conferenceISPRS Workshop Laser Scanning 2013
dcterms.source.conference-start-dateNov 11 2013
dcterms.source.conferencelocationAntalya, Turkey
dcterms.source.place-
curtin.department
curtin.accessStatusOpen access via publisher


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