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

编号 030025905

推送时间 20201005

研究领域 森林经理 

年份 2020 

类型 期刊 

语种 英语

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

来源期刊 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

期 第259期

发表时间 19050712

关键词 Vegetation;  Three-dimensional displays;  Forestry;  Laser radar;  Clustering algorithms;  Remote sensing;  Image segmentation;  Airborne LiDAR;  canopy height model (CHM);  individual tree segmentation;  profile analysis;  watershed algorithm; 

摘要 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.

服务人员 付贺龙

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