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Open AccessArticle

A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas

1
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstr. 7, D-76131 Karlsruhe, Germany
2
Univ. Paris-Est, LASTIG MATIS, IGN, ENSG, 73 avenue de Paris, F-94160 Saint-Mandé, France
3
Institute of Computer Science II, University of Bonn, Friedrich-Ebert-Allee 144, D-53113 Bonn, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Norman Kerle, Markus Gerke, Sébastien Lefèvre, Clement Atzberger and Prasad S. Thenkabail
Remote Sens. 2017, 9(3), 277; https://doi.org/10.3390/rs9030277
Received: 29 December 2016 / Revised: 6 March 2017 / Accepted: 9 March 2017 / Published: 16 March 2017
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 Delft in the Netherlands. This dataset contains 10.13 M labeled 3D points among which 17.6 % are labeled as “tree points”. The derived results clearly reveal a semantic classification of high accuracy (up to 90.77 %) and an instance-level segmentation of high plausibility, while the simplicity, applicability and efficiency of the involved methods even allow applying the complete framework on a standard laptop computer with a reasonable processing time (less than 2.5 h). View Full-Text
Keywords: mobile mapping systems; 3D point cloud; feature extraction; feature selection; semantic classification; semantic segmentation; instance-level segmentation; tree-like objects mobile mapping systems; 3D point cloud; feature extraction; feature selection; semantic classification; semantic segmentation; instance-level segmentation; tree-like objects
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MDPI and ACS Style

Weinmann, M.; Weinmann, M.; Mallet, C.; Brédif, M. A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas. Remote Sens. 2017, 9, 277. https://doi.org/10.3390/rs9030277

AMA Style

Weinmann M, Weinmann M, Mallet C, Brédif M. A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas. Remote Sensing. 2017; 9(3):277. https://doi.org/10.3390/rs9030277

Chicago/Turabian Style

Weinmann, Martin; Weinmann, Michael; Mallet, Clément; Brédif, Mathieu. 2017. "A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas" Remote Sens. 9, no. 3: 277. https://doi.org/10.3390/rs9030277

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