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Article

Filtering Stems and Branches from Terrestrial Laser Scanning Point Clouds Using Deep 3-D Fully Convolutional Networks

Department of Geography, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
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Remote Sens. 2018, 10(8), 1215; https://doi.org/10.3390/rs10081215
Received: 7 June 2018 / Revised: 24 July 2018 / Accepted: 29 July 2018 / Published: 2 August 2018
(This article belongs to the Special Issue Remote Sensing to Assess Canopy Structure and Function)
Terrestrial laser scanning (TLS) can produce precise and detailed point clouds of forest environment, thus enabling quantitative structure modeling (QSM) for accurate tree morphology and wood volume allocation. Applying QSM to plot-scale wood delineation is highly dependent on wood visibility from forest scans. A common problem is to filter wood point from noisy leafy points in the crowns and understory. This study proposed a deep 3-D fully convolution network (FCN) to filter both stem and branch points from complex plot scans. To train the 3-D FCN, reference stem and branch points were delineated semi-automatically for 14 sampled areas and three common species. Among seven testing areas, agreements between reference and model prediction, measured by intersection over union (IoU) and overall accuracy (OA), were 0.89 (stem IoU), 0.54 (branch IoU), 0.79 (mean IoU), and 0.94 (OA). Wood filtering results were further incorporated to a plot-scale QSM to extract individual tree forms, isolated wood, and understory wood from three plot scans with visual assessment. The wood filtering experiment provides evidence that deep learning is a powerful tool in 3-D point cloud processing and parsing. View Full-Text
Keywords: TLS; QSM; CNN; FCN; deep learning; 3-D point clouds; forest; segmentation; tree structure TLS; QSM; CNN; FCN; deep learning; 3-D point clouds; forest; segmentation; tree structure
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MDPI and ACS Style

Xi, Z.; Hopkinson, C.; Chasmer, L. Filtering Stems and Branches from Terrestrial Laser Scanning Point Clouds Using Deep 3-D Fully Convolutional Networks. Remote Sens. 2018, 10, 1215. https://doi.org/10.3390/rs10081215

AMA Style

Xi Z, Hopkinson C, Chasmer L. Filtering Stems and Branches from Terrestrial Laser Scanning Point Clouds Using Deep 3-D Fully Convolutional Networks. Remote Sensing. 2018; 10(8):1215. https://doi.org/10.3390/rs10081215

Chicago/Turabian Style

Xi, Zhouxin, Chris Hopkinson, and Laura Chasmer. 2018. "Filtering Stems and Branches from Terrestrial Laser Scanning Point Clouds Using Deep 3-D Fully Convolutional Networks" Remote Sensing 10, no. 8: 1215. https://doi.org/10.3390/rs10081215

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