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Remote Sens. 2018, 10(2), 335; https://doi.org/10.3390/rs10020335

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

1
Geographic Information Systems Laboratory, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
2
Laboratory of Geo-information Science and Remote Sensing, Wageningen University, 6708 PB Wageningen, The Netherlands
*
Author to whom correspondence should be addressed.
Received: 14 November 2017 / Revised: 9 February 2018 / Accepted: 21 February 2018 / Published: 23 February 2018
(This article belongs to the Special Issue Lidar for Forest Science and Management)
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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 segmentation; uncertainty; airborne LiDAR; ensemble filtering; 3D tree delineation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Parkan, M.; Tuia, D. Estimating Uncertainty of Point-Cloud Based Single-Tree Segmentation with Ensemble Based Filtering. Remote Sens. 2018, 10, 335.

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