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

Published on
March 13, 2018

Matthew Parkan and Devis Tuia published an article: Estimating Uncertainty of Point-Cloud Based Single-Tree Segmentation with Ensemble Based Filtering in Remote Sensing 2018, 10(2), 335.

doi:10.3390/rs10020335

Abstract
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. View Full-Text

Keywords: segmentation; uncertainty; airborne LiDAR; ensemble filtering; 3D tree delineation