使用机载 LiDAR 点云数据进行单棵树分割的基于 Nyström 的光谱聚类

The spectral clustering method has notable advantages in segmentation.But the high computational complexity and time consuming limit its application in large-scale and dense airborne Light Detection and Ranging (LiDAR) point cloud data. We proposed the Nyström-based spectral clustering (NSC) algorithm to decrease the computational burden. This novel NSC method showed accurate and rapid in individual tree segmentation using point cloud data. The K-nearest neighbour-based sampling (KNNS) was proposed for the Nyström approximation of voxels to improve the efficiency. The NSC algorithm showed good performance for 32 plots in China and Europe.

谱聚类（spectral clustering）方法在分割任务中具备显著优势。但该方法存在计算复杂度高、耗时较长的问题，限制了其在大规模密集型机载激光雷达（Light Detection and Ranging, LiDAR）点云数据中的应用。为此，本文提出了基于尼斯特伦（Nyström）的谱聚类（Nyström-based spectral clustering, NSC）算法以降低计算负担。该新型NSC算法在利用点云数据进行单木分割任务时，展现出精准快速的良好性能。为提升尼斯特伦近似的效率，本文提出了基于K近邻（K-nearest neighbour）的采样（K-nearest neighbour-based sampling, KNNS）方法用于体素的尼斯特伦近似。该NSC算法在中国和欧洲的共计32个样地中均取得了优异的分割表现。

搜集汇总

背景与挑战

背景概述

该数据集提出了一种基于Nyström近似的光谱聚类算法，用于从机载LiDAR点云数据中高效分割单棵树，以降低计算负担并提升处理速度。它属于'落叶松高效培育技术研究'项目，数据以文档格式提供，具有较高的准确性和可靠性。