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RSS FeedsRemote Sensing, Vol. 10, Pages 1215: Filtering Stems and Branches from Terrestrial Laser Scanning Point Clouds Using Deep 3-D Fully Convolutional Networks (Remote Sensing)

 
 

15 august 2018 10:01:21

 
Remote Sensing, Vol. 10, Pages 1215: Filtering Stems and Branches from Terrestrial Laser Scanning Point Clouds Using Deep 3-D Fully Convolutional Networks (Remote Sensing)
 


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.


 
67 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 10, Pages 1216: UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health (Remote Sensing)
Remote Sensing, Vol. 10, Pages 1214: Detailed Land Cover Mapping from Multitemporal Landsat-8 Data of Different Cloud Cover (Remote Sensing)
 
 
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