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

Zhouxin Xi,Laura Chasmer,Chris Hopkinson

doi:10.3390/rs10081215

Abstract

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.

Highlights

Modern wood management is a balance of economic activities and environmental stewardships
A 3-D fully convolution network (FCN) structure was customized in this study, aiming to filter stems and branches from Terrestrial laser scanning (TLS) point clouds in a robust and flexible manner
We hope to unveil the potential of applying deep learning methods in complicated 3-D processing tasks, in parsing forest plot scans, in pursuit of automatic and intelligent wood resource management

Summary

Introduction

Modern wood management is a balance of economic activities and environmental stewardships. The convolutional layers are termed Convolutional Neural Network (CNN) layers Such a layer configuration of FCN has had significant success in practice for the capability of automatic feature selection and high-degree nonlinearity. A 3-D FCN structure was customized in this study, aiming to filter stems and branches from TLS point clouds in a robust and flexible manner. The filtering results from 3-D FCN were input to a QSM model to test application possibility for plot-level wood delineation. From this experimental study, we hope to unveil the potential of applying deep learning methods in complicated 3-D processing tasks, in parsing forest plot scans, in pursuit of automatic and intelligent wood resource management

Data and Sampling

Labeling Reference Points

Reconstructing 3-D Tree Geometries

Results and Discussion

Wood Reconstruction

Conclusions