Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 411-417, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-411-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
09 Jun 2016
FAST AND ROBUST STEM RECONSTRUCTION IN COMPLEX ENVIRONMENTS USING TERRESTRIAL LASER SCANNING
D. Wang1, M. Hollaus1, E. Puttonen2,3, and N. Pfeifer1 1Department of Geodesy and Geoinformation, TU Wien, Vienna 1040, Austria
2Centre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute (FGI), Masala 02431, Finland
3Department of Remote Sensing and Photogrammetry, National Land Survey of Finland, Finnish Geospatial Research Institute (FGI), Masala 02431, Finland
Keywords: Terrestrial Laser Scanning, Stem Detection, Cylinder Fitting, RANSAC, Self-adaptive Cylinder Growing, Point Cloud Sampling Abstract. Terrestrial Laser Scanning (TLS) is an effective tool in forest research and management. However, accurate estimation of tree parameters still remains challenging in complex forests. In this paper, we present a novel algorithm for stem modeling in complex environments. This method does not require accurate delineation of stem points from the original point cloud. The stem reconstruction features a self-adaptive cylinder growing scheme. This algorithm is tested for a landslide region in the federal state of Vorarlberg, Austria. The algorithm results are compared with field reference data, which show that our algorithm is able to accurately retrieve the diameter at breast height (DBH) with a root mean square error (RMSE) of ~1.9 cm. This algorithm is further facilitated by applying an advanced sampling technique. Different sampling rates are applied and tested. It is found that a sampling rate of 7.5% is already able to retain the stem fitting quality and simultaneously reduce the computation time significantly by ~88%.
Conference paper (PDF, 3068 KB)


Citation: Wang, D., Hollaus, M., Puttonen, E., and Pfeifer, N.: FAST AND ROBUST STEM RECONSTRUCTION IN COMPLEX ENVIRONMENTS USING TERRESTRIAL LASER SCANNING, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 411-417, https://doi.org/10.5194/isprs-archives-XLI-B3-411-2016, 2016.

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