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A classification-segmentation framework for the detection of individual trees in dense MMS point cloud data acquired in urban areas

Weinmann, M.; Weinmann, M.; Mallet, C.; Brédif, M.

In this paper, we present a novel framework for detecting individual trees in densely sampled 3D point cloud data acquired in urban areas. Given a 3D point cloud, the objective is to assign point-wise labels that are both class-aware and instance-aware, a task that is known as instance-level segmentation. To achieve this, our framework addresses two successive steps. The first step of our framework is given by the use of geometric features for a binary point-wise semantic classification with the objective of assigning semantic class labels to irregularly distributed 3D points, whereby the labels are defined as “tree points” and “other points”. The second step of our framework is given by a semantic segmentation with the objective of separating individual trees within the “tree points”. This is achieved by applying an efficient adaptation of the mean shift algorithm and a subsequent segment-based shape analysis relying on semantic rules to only retain plausible tree segments. We demonstrate the performance of our framework on a publicly available benchmark dataset, which has been acquired with a mobile mapping system in the city of D ... mehr

Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Zeitschriftenaufsatz
Jahr 2017
Sprache Englisch
Identifikator DOI: 10.3390/rs9030277
ISSN: 2072-4292
URN: urn:nbn:de:swb:90-703996
KITopen ID: 1000070399
HGF-Programm 12.01.01; LK 01
Erschienen in Remote sensing
Band 9
Heft 3
Seiten Art. Nr.: 277
Lizenz CC BY 4.0: Creative Commons Namensnennung 4.0 International
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Schlagworte mobile mapping systems; 3D point cloud; feature extraction; feature selection; semantic classification; semantic segmentation; instance-level segmentation; tree-like objects
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