Hierarchical higher order crf for the classification of airborne lidar point clouds in urban areas

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dc.identifier.uri http://dx.doi.org/10.15488/699
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/723
dc.contributor.author Niemeyer, Joachim
dc.contributor.author Rottensteiner, Franz
dc.contributor.author Sörgel, Uwe
dc.contributor.author Heipke, Christian
dc.date.accessioned 2016-11-21T07:54:41Z
dc.date.available 2016-11-21T07:54:41Z
dc.date.issued 2016
dc.identifier.citation Niemeyer, J.; Rottensteiner, F.; Soergel, U.; Heipke, C.: Hierarchical higher order crf for the classification of airborne lidar point clouds in urban areas. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 41 (2016), S. 655-662. DOI: http://dx.doi.org/10.5194/isprsarchives-XLI-B3-655-2016
dc.description.abstract We propose a novel hierarchical approach for the classification of airborne 3D lidar points. Spatial and semantic context is incorporated via a two-layer Conditional Random Field (CRF). The first layer operates on a point level and utilises higher order cliques. Segments are generated from the labelling obtained in this way. They are the entities of the second layer, which incorporates larger scale context. The classification result of the segments is introduced as an energy term for the next iteration of the point-based layer. This framework iterates and mutually propagates context to improve the classification results. Potentially wrong decisions can be revised at later stages. The output is a labelled point cloud as well as segments roughly corresponding to object instances. Moreover, we present two new contextual features for the segment classification: the distance and the orientation of a segment with respect to the closest road. It is shown that the classification benefits from these features. In our experiments the hierarchical framework improve the overall accuracies by 2.3% on a point-based level and by 3.0% on a segment-based level, respectively, compared to a purely point-based classification. eng
dc.language.iso eng
dc.publisher Göttingen : Copernicus GmbH
dc.relation.ispartof 23rd International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Congress, ISPRS 2016, 12–19 July 2016, Prague, Czech Republic
dc.relation.ispartofseries International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 41 (2016)
dc.rights CC BY 3.0 Unported
dc.rights.uri http://creativecommons.org/licenses/by/3.0/
dc.subject Classification eng
dc.subject Contextual eng
dc.subject Higher Order Random Fields eng
dc.subject Lidar eng
dc.subject Point Cloud eng
dc.subject Urban eng
dc.subject Optical radar eng
dc.subject Random processes eng
dc.subject Remote sensing eng
dc.subject Semantics eng
dc.subject Classification results eng
dc.subject Conditional random field eng
dc.subject Contextual eng
dc.subject Contextual feature eng
dc.subject Hierarchical approach eng
dc.subject Point cloud eng
dc.subject Random fields eng
dc.subject Urban eng
dc.subject Classification (of information) eng
dc.subject.ddc 500 | Naturwissenschaften ger
dc.subject.ddc 520 | Astronomie, Kartographie ger
dc.title Hierarchical higher order crf for the classification of airborne lidar point clouds in urban areas
dc.type article
dc.type conferenceObject
dc.type Text
dc.relation.issn 1682-1750
dc.relation.doi http://dx.doi.org/10.5194/isprsarchives-XLI-B3-655-2016
dc.bibliographicCitation.volume 41
dc.bibliographicCitation.firstPage 655
dc.bibliographicCitation.lastPage 662
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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