Processing tree point clouds using Gaussian Mixture Models

23 citations as recorded by crossref.

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4. A Deep Learning Time Series Approach for Leaf and Wood Classification from Terrestrial LiDAR Point Clouds T. Han & G. Sánchez-Azofeifa https://doi.org/10.3390/rs14133157
5. SegmentedForests: a labelled dataset of terrestrial LiDAR point clouds for semantic segmentation of forests D. Laino et al. https://doi.org/10.1093/forestry/cpaf062
6. A new approach to retrieve leaf normal distribution using terrestrial laser scanners S. Jin et al. https://doi.org/10.1007/s11676-015-0204-z
7. Analyzing the Vertical Distribution of Crown Material in Mixed Stand Composed of Two Temperate Tree Species O. Martin-Ducup et al. https://doi.org/10.3390/f9110673
8. WLC-Net: A Robust and Fast Deep Learning Wood–Leaf Classification Method H. Li et al. https://doi.org/10.3390/f16030513
9. Highly Accurate Tree Models Derived from Terrestrial Laser Scan Data: A Method Description J. Hackenberg et al. https://doi.org/10.3390/f5051069
10. Separating Tree Photosynthetic and Non-Photosynthetic Components from Point Cloud Data Using Dynamic Segment Merging D. Wang et al. https://doi.org/10.3390/f9050252
11. Individual Tree Canopy Parameters Estimation Using UAV-Based Photogrammetric and LiDAR Point Clouds in an Urban Park E. Ghanbari Parmehr & M. Amati https://doi.org/10.3390/rs13112062
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14. The utility of terrestrial photogrammetry for assessment of tree volume and taper in boreal mixedwood forests C. Mulverhill et al. https://doi.org/10.1007/s13595-019-0852-9
15. Modelling and Design of Habitat Features: Will Manufactured Poles Replace Living Trees as Perch Sites for Birds? A. Holland et al. https://doi.org/10.3390/su15097588
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17. Improved Supervised Learning-Based Approach for Leaf and Wood Classification From LiDAR Point Clouds of Forests S. Krishna Moorthy et al. https://doi.org/10.1109/TGRS.2019.2947198
18. LiDAR Point Clouds to 3-D Urban Models$:$ A Review R. Wang et al. https://doi.org/10.1109/JSTARS.2017.2781132
19. Extraction of Liana Stems Using Geometric Features from Terrestrial Laser Scanning Point Clouds T. Han & G. Sánchez-Azofeifa https://doi.org/10.3390/rs14164039
20. Separating Leaf and Wood Points in Terrestrial Laser Scanning Data Using Multiple Optimal Scales J. Zhou et al. https://doi.org/10.3390/s19081852
21. Extraction of tubular shapes from dense point clouds and application to tree reconstruction from laser scanned data J. Ravaglia et al. https://doi.org/10.1016/j.cag.2017.05.016
22. Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data J. Ravaglia et al. https://doi.org/10.3390/f10070599
23. Terrestrial laser scanning to quantify above-ground biomass of structurally complex coastal wetland vegetation C. Owers et al. https://doi.org/10.1016/j.ecss.2018.02.027