Abstract

Developing a robust point cloud segmentation algorithm for individual trees from an amount of point cloud data has great significance for tracking tree changes. This method can measure the size, growth, and mortality of individual trees to track and understand forest carbon storage and variation. Traditional measurement methods are not only slow but also tardy. In order to obtain forest information better and faster, this article focuses on two aspects: The first is using UAVs to obtain multiview remote sensing images of the forest, and then using the structure from motion algorithm to construct the forest sparse point cloud and patch-based MVS algorithm to construct the dense point cloud. The second is that a targeted point cloud deep learning method is proposed to extract the point cloud of a single tree. The research results show that the accuracy of single-tree point cloud segmentation of deep learning methods is more than 90%, and the accuracy is far better than traditional planar image segmentation and point cloud segmentation. The combination of point cloud data acquisition with UAV remote sensing and point cloud deep learning algorithms can meet the needs of forestry surveys. It is undeniable that this method, as a forestry survey tool, has a large space for promotion and possible future development.

Highlights

  • F OREST is the most distributed natural ecological resource on the land, with the most complex structure and the richest biomass

  • We have not achieved the accuracy of traditional 2-D orthoimage segmentation, point cloud data has more information, and point cloud segmentation is more meaningful than orthoimage segmentation because we can get a series of information that cannot be obtained from 2-D photos such as height and DBH

  • The point cloud information obtained by radar equipment is very accurate, few people can use it because of the high cost of radar equipment

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Summary

INTRODUCTION

F OREST is the most distributed natural ecological resource on the land, with the most complex structure and the richest biomass. The former scans the data over the woodland by means of a drone, while the latter scans the forest under the forest by hand or on a woodland trolley [8] The former has great advantages in obtaining high-level information of forest land, such as the acquisition of high information of canopy trees. Because the UAV visible light equipment has the low-cost feature, the research obtains point cloud data by the UAV multiview matching algorithm [15]. For the disorder of point cloud data due to its sparseness and limited data volume, a point-net model was proposed to segment the point cloud data [24] In this experiment, we modify the algorithm to make it more suitable for forest tree point cloud segmentation to achieve good accuracy

RESEARCH ON METHOD OF OBTAINING RAW DATA
Experiment Time and Place
UAV Data Acquisition
Point Cloud Data
Extraction of Feature Points
Feature Points Matching
SFM Algorithm for Sparse Point Cloud
PMVS Algorithm for Dense Point Cloud
POINT CLOUD SEGMENTATION ALGORITHM
Findings
CONCLUSION
Full Text
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