Estimating Uncertainty of Point-Cloud Based Single-Tree Segmentation with Ensemble Based Filtering

Matthew Parkan*, Devis Tuia

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)


Individual tree crown segmentation from Airborne Laser Scanning data is a nodal problem in forest remote sensing. Focusing on single layered spruce and fir dominated coniferous forests, this article addresses the problem of directly estimating 3D segment shape uncertainty (i.e., without field/reference surveys), using a probabilistic approach. First, a coarse segmentation (marker controlled watershed) is applied. Then, the 3D alpha hull and several descriptors are computed for each segment. Based on these descriptors, the alpha hulls are grouped to form ensembles (i.e., groups of similar tree shapes). By examining how frequently regions of a shape occur within an ensemble, it is possible to assign a shape probability to each point within a segment. The shape probability can subsequently be thresholded to obtain improved (filtered) tree segments. Results indicate this approach can be used to produce segmentation reliability maps. A comparison to manually segmented tree crowns also indicates that the approach is able to produce more reliable tree shapes than the initial (unfiltered) segmentation.
Original languageEnglish
Article number335
JournalRemote Sensing
Issue number2
Publication statusPublished - 23 Feb 2018


  • 3D tree delineation
  • Airborne LiDAR
  • Ensemble filtering
  • Segmentation
  • Uncertainty


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