文献类型：期刊文献

中文题名：Nystr?m-based spectral clustering using airborne LiDAR point cloud data for individual tree segmentation

作者：Yong Pang[1,2] Weiwei Wang[1,3] Liming Du[1,2] Zhongjun Zhang[3] Xiaojun Liang[1,2] Yongning Li[4] Zuyuan Wang[5]

第一作者：Yong Pang;庞勇

机构：[1]Institute of Forest Resource Information Techniques,Chinese Academy of Forestry,Beijing,People’s Republic of China;[2]Key Laboratory of Forestry Remote Sensing and Information System of National Forestry and Grassland Administration,Beijing,People’s Republic of China;[3]College of Artificial Intelligence,Beijing Normal University,Beijing,People’s Republic of China;[4]College of Forestry,Hebei Agricultural University,Baoding,People’s Republic of China;[5]Department of Land Change Science,Swiss Federal Institute for Forest,Snow and Landscape Research WSL,Birmensdorf,Switzerland

年份：2021

卷号：14

期号：10

起止页码：1452-1476

中文期刊名：国际数字地球学报（英文）

外文期刊名：International Journal of Digital Earth

收录：Scopus;PubMed

语种：英文

中文关键词：Tree segmentation;airborne LiDAR;spectral clustering;Nystr?m approximation;sampling method

分类号：TP3

摘要：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 Nystr?m-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 Nystr?m approximation of voxels to improve the efficiency.The NSC algorithm showed good performance for 32 plots in China and Europe.The overall matching rate and extraction rate of proposed algorithm reached 69%and 103%.For all trees located by Global Navigation Satellite System(GNSS)calibrated tape-measures,the tree height regression of the matching results showed an value of 0.88 and a relative root mean square error(RMSE)of 5.97%.For all trees located by GNSS calibrated total-station measures,the values were 0.89 and 4.49%.The method also showed good performance in a benchmark dataset with an improvement of 7%for the average matching rate.The results demonstrate that the proposed NSC algorithm provides an accurate individual tree segmentation and parameter estimation using airborne LiDAR point cloud data.