Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74179
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.creatorYao, Wen_US
dc.creatorPolewski, Pen_US
dc.creatorKrzystek, Pen_US
dc.date.accessioned2018-03-29T07:16:19Z-
dc.date.available2018-03-29T07:16:19Z-
dc.identifier.issn1682-1750en_US
dc.identifier.urihttp://hdl.handle.net/10397/74179-
dc.descriptionISPRS Geospatial Week 2017, 18 - 22 September 2017en_US
dc.language.isoenen_US
dc.publisherCopernicus GmbHen_US
dc.rights© Authors 2017. CC BY 4.0 License.en_US
dc.subjectEvidence fusionen_US
dc.subjectObject classificationen_US
dc.subjectProbabilistic graph modelsen_US
dc.subjectUltra dense MLSen_US
dc.subjectUrban road corridoren_US
dc.titleSemantic labelling of ultra dense MLS point clouds in urban road corridors based on fusing CRF with shape priorsen_US
dc.typeConference Paperen_US
dc.identifier.spage971en_US
dc.identifier.epage976en_US
dc.identifier.volume42en_US
dc.identifier.issue2W7en_US
dc.identifier.doi10.5194/isprs-archives-XLII-2-W7-971-2017en_US
dcterms.abstractIn this paper, a labelling method for the semantic analysis of ultra-high point density MLS data (up to 4000 points/m2) in urban road corridors is developed based on combining a conditional random field (CRF) for the context-based classification of 3D point clouds with shape priors. The CRF uses a Random Forest (RF) for generating the unary potentials of nodes and a variant of the contrast-sensitive Potts model for the pair-wise potentials of node edges. The foundations of the classification are various geometric features derived by means of co-variance matrices and local accumulation map of spatial coordinates based on local neighbourhoods. Meanwhile, in order to cope with the ultra-high point density, a plane-based region growing method combined with a rule-based classifier is applied to first fix semantic labels for man-made objects. Once such kind of points that usually account for majority of entire data amount are pre-labeledMergeCell the CRF classifier can be solved by optimizing the discriminative probability for nodes within a subgraph structure excluded from pre-labeled nodes. The process can be viewed as an evidence fusion step inferring a degree of belief for point labelling from different sources. The MLS data used for this study were acquired by vehicle-borne Z+F phase-based laser scanner measurement, which permits the generation of a point cloud with an ultra-high sampling rate and accuracy. The test sites are parts of Munich City which is assumed to consist of seven object classes including impervious surfaces, tree, building roof/facade, low vegetation, vehicle and pole. The competitive classification performance can be explained by the diverse factors: e.g. the above ground height highlights the vertical dimension of houses, trees even cars, but also attributed to decision-level fusion of graph-based contextual classification approach with shape priors. The use of context-based classification methods mainly contributed to smoothing of labelling by removing outliers and the improvement in underrepresented object classes. In addition, the routine operation of a context-based classification for such high density MLS data becomes much more efficient being comparable to non-contextual classification schemes.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationInternational archives of the photogrammetry, remote sensing and spatial information sciences, 2017, v. 42, no. 2W7, p. 971-976en_US
dcterms.isPartOfInternational archives of the photogrammetry, remote sensing and spatial information sciencesen_US
dcterms.issued2017-
dc.identifier.scopus2-s2.0-85031037514-
dc.relation.conferenceISPRS Geospatial Weeken_US
dc.identifier.eissn2194-9034en_US
dc.identifier.rosgroupid2017002143-
dc.description.ros2017-2018 > Academic research: refereed > Publication in refereed journalen_US
dc.description.validate201802 bcrcen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
Appears in Collections:Conference Paper
Files in This Item:
File Description SizeFormat 
isprs-archives-XLII-2-W7-971-2017.pdf2.99 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

86
Last Week
0
Last month
Citations as of Oct 1, 2023

Downloads

11
Citations as of Oct 1, 2023

SCOPUSTM   
Citations

9
Last Week
0
Last month
Citations as of Sep 28, 2023

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.