An Individual Tree Segmentation Method Based on Watershed Algorithm and Three-Dimensional Spatial Distribution Analysis From Airborne LiDAR Point Clouds

Accurate individual tree segmentation is an important basis for the subsequent calculation and analysis of forestry parameters. However, rasterized canopy height model based methods often suffer from 3-D information loss due to the interpolation operation. Therefore, this article proposes an individ...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2020, Vol.13, p.1055-1067
Main Authors: Yang, Juntao, Kang, Zhizhong, Cheng, Sai, Yang, Zhou, Akwensi, Perpetual Hope
Format: Article
Language:English
Subjects:
Airborne LiDAR
Algorithms
Analysis
canopy height model (CHM)
Cluster analysis
Clustering
Clustering algorithms
Distribution
Forestry
Image segmentation
individual tree segmentation
Interpolation
Laser radar
Lidar
Markers
Plant cover
Principal components analysis
profile analysis
Remote sensing
Spatial analysis
Spatial distribution
Three dimensional analysis
Three dimensional models
Three-dimensional displays
Trees
Vector quantization
Vegetation
watershed algorithm
Watersheds
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Summary:Accurate individual tree segmentation is an important basis for the subsequent calculation and analysis of forestry parameters. However, rasterized canopy height model based methods often suffer from 3-D information loss due to the interpolation operation. Therefore, this article proposes an individual tree segmentation method based on the marker-controlled watershed algorithm and 3-D spatial distribution analysis from airborne LiDAR point clouds. First, based on the potential tree apices derived from the local maxima filtering, the marker-controlled watershed segmentation algorithm is conducted to obtain the coarse point clusters. Then, within the principal component analysis defined local coordinate reference framework, a multidirectional 3-D spatial profile analysis is performed on each point cluster to refine the potential tree apex positions. Finally, the refined potential tree apex positions are used as a prior of K-means clustering to achieve the coarse-to-fine individual tree segmentation. Comparative experiments were conducted on the public NEWFOR dataset to evaluate the proposed method. Results indicate that the proposed method is efficient and robust for segmenting individual trees.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2020.2979369