Classification of airborne 3D point clouds regarding separation of vegetation in complex environments

Dimitri Bulatov, Dominik Stütz, Jorg Hacker, Martin Weinmann

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Classification of outdoor point clouds is an intensely studied topic, particularly with respect to the separation of vegetation from the terrain and manmade structures. In the presence of many overhanging and vertical structures, the (relative) height is no longer a reliable criterion for such a separation. An alternative would be to apply supervised classification; however, thousands of examples are typically required for appropriate training. In this paper, an unsupervised and rotation-invariant method is presented and evaluated for three datasets with very different characteristics. The method allows us to detect planar patches by filtering and clustering so-called superpoints, whereby the well-known but suitably modified random sampling and consensus (RANSAC) approach plays a key role for plane estimation in outlier-rich data. The performance of our method is compared to that produced by supervised classifiers common for remote sensing settings: random forest as learner and feature sets for point cloud processing, like covariance-based features or point descriptors. It is shown that for point clouds resulting from airborne laser scans, the detection accuracy of the proposed method is over 96% and, as such, higher than that of standard supervised classification approaches. Because of artifacts caused by interpolation during 3D stereo matching, the overall accuracy was lower for photogrammetric point clouds (74-77%). However, using additional salient features, such as the normalized green-red difference index, the results became more accurate and less dependent on the data source.

Original languageEnglish
Pages (from-to)F6-F20
Number of pages15
JournalApplied Optics
Volume60
Issue number22
Early online date17 May 2021
DOIs
Publication statusPublished - 1 Aug 2021

Keywords

  • remote sensing
  • random forest
  • 3D point clouds
  • vegetation

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