Contextual classification of point cloud data by exploiting individual 3d neigbourhoods

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dc.identifier.uri http://dx.doi.org/10.15488/1079
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/1103
dc.contributor.author Weinmann, M.
dc.contributor.author Schmidt, Alena
dc.contributor.author Mallet, C.
dc.contributor.author Hinz, Stefan
dc.contributor.author Rottensteiner, Franz
dc.contributor.author Jutzi, B.
dc.date.accessioned 2017-02-02T13:57:10Z
dc.date.available 2017-02-02T13:57:10Z
dc.date.issued 2015
dc.identifier.citation Weinmann, M.; Schmidt, A.; Mallet, C.; Hinz, S.; Rottensteiner, F. et al.: Contextual classification of point cloud data by exploiting individual 3d neigbourhoods. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-3 (2015), Nr. W4, S. 271-278. DOI: https://doi.org/10.5194/isprsannals-II-3-W4-271-2015
dc.description.abstract The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification. eng
dc.language.iso eng
dc.publisher Göttingen : Copernicus GmbH
dc.relation.ispartof Joint ISPRS Conference on Photogrammetric Image Analysis (PIA) and High Resolution Earth Imaging for Geospatial Information (HRIGI), March 25-27, 2015, Munich, Germany
dc.relation.ispartofseries ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-3 (2015), Nr. W4
dc.rights CC BY 3.0
dc.rights.uri https://creativecommons.org/licenses/by/3.0/
dc.subject Lidar eng
dc.subject Laser Scanning eng
dc.subject Point Cloud eng
dc.subject Features eng
dc.subject Classification eng
dc.subject Contextual Learning eng
dc.subject 3D Scene Analysis eng
dc.subject Urban eng
dc.subject laser-scanning data eng
dc.subject form lidar data eng
dc.subject markov networks eng
dc.subject random-fields eng
dc.subject segmentation eng
dc.subject airborne eng
dc.subject.ddc 550 | Geowissenschaften ger
dc.title Contextual classification of point cloud data by exploiting individual 3d neigbourhoods
dc.type article
dc.type conferenceObject
dc.type Text
dc.relation.issn 2194-9034
dc.relation.doi https://doi.org/10.5194/isprsannals-II-3-W4-271-2015
dc.bibliographicCitation.issue W4
dc.bibliographicCitation.volume II-3
dc.bibliographicCitation.firstPage 271
dc.bibliographicCitation.lastPage 278
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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