CLASSIFICATION of MLS POINT CLOUDS in URBAN SCENES USING DETRENDED GEOMETRIC FEATURES from SUPERVOXEL-BASED LOCAL CONTEXTS

Z. Sun, Y. Xu, L. Hoegner, U. Stilla

Research output: Contribution to journalConference articlepeer-review

29 Scopus citations

Abstract

In this work, we propose a classification method designed for the labeling of MLS point clouds, with detrended geometric features extracted from the points of the supervoxel-based local context. To achieve the analysis of complex 3D urban scenes, acquired points of the scene should be tagged with individual labels of different classes. Thus, assigning a unique label to the points of an object that belong to the same category plays an essential role in the entire 3D scene analysis workflow. Although plenty of studies in this field have been reported, this work is still a challenging task. Specifically, in this work: 1) A novel geometric feature extraction method, detrending the redundant and in-salient information in the local context, is proposed, which is proved to be effective for extracting local geometric features from the 3D scene. 2) Instead of using individual point as basic element, the supervoxel-based local context is designed to encapsulate geometric characteristics of points, providing a flexible and robust solution for feature extraction. 3) Experiments using complex urban scene with manually labeled ground truth are conducted, and the performance of proposed method with respect to different methods is analyzed. With the testing dataset, we have obtained a result of 0.92 for overall accuracy for assigning eight semantic classes.

Original languageEnglish
Pages (from-to)271-278
Number of pages8
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume4
Issue number2
DOIs
StatePublished - 28 May 2018
Event2018 ISPRS TC II Mid-term Symposium "Towards Photogrammetry 2020" - Riva del Garda, Italy
Duration: 4 Jun 20187 Jun 2018

Keywords

  • Classification
  • MLS
  • detrended geometric features
  • local context
  • supervoxel

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