A Method Based on Improved iForest for Trunk Extraction and Denoising of Individual Street Trees

Abstract

Currently, the street tree resource survey using Mobile laser scanning (MLS) represents a hot spot around the world. Refined trunk extraction is an essential step for 3D reconstruction of street trees. However, due to scanning errors and the effects of occlusion by various types of features in the urban environment, street tree point cloud data processing has the problem of excessive noise. For the noise points that are difficult to remove using statistical methods in close proximity to the tree trunk, we propose an adaptive trunk extraction and denoising method for street trees based on an improved iForest (Isolation Forest) algorithm. Firstly, to extract the individual tree trunk points, the trunk and the crown are distinguished from the individual tree point cloud through point cloud slicing. Next, the iForest algorithm is improved by conducting automatic calculation of the contamination and further used to denoise the tree trunk point cloud. Finally, the method is validated with five datasets of different scenes. The results indicate that our method is robust and effective in extracting and denoising tree trunks. Compared with the traditional Statistical Outlier Removal (SOR) filter and Radius filter denoising methods, the denoising accuracy of the proposed method can be improved by approximately 30% for noise points close to tree trunks. Compared to iForest, the proposed method automatically calculates the contamination, improving the automation of the algorithm. Our method can provide more precise trunk point clouds for 3D reconstruction of street trees.

Plant phenotyping relevance

街路樹の個体別幹点群を抽出・ノイズ除去する手法を開発し、複数データセットで検証しており、植物形態（幹）の取得が研究の中心である。

Code and data availability

The supplied blocks describe five MLS point cloud datasets (Paris-Lille-3D, Trimble MX9, GEOSLAM ZEB-Horizon) and an improved iForest trunk extraction/denoising method, but contain no data availability statement, no public repository deposit of the paper's point cloud data or derived measurements, and no author code,脚本

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