文献类型：期刊文献

英文题名：Nystrom-based spectral clustering using airborne LiDAR point cloud data for individual tree segmentation

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

第一作者：Pang, Yong;庞勇

通信作者：Pang, Y[1];Pang, Y[2]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Natl Forestry & Grassland Adm, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China;[3]Beijing Normal Univ, Coll Artificial Intelligence, Beijing, Peoples R China;[4]Hebei Agr Univ, Coll Forestry, Baoding, Peoples R China;[5]Swiss Fed Inst Forest Snow & Landscape Res WSL, Dept Land Change Sci, Birmensdorf, Switzerland

年份：2021

卷号：14

期号：10

起止页码：1452-1476

外文期刊名：INTERNATIONAL JOURNAL OF DIGITAL EARTH

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000668013500001)】；

基金：This study was funded by the National Key Research and Development Program of China [grant number 2017YFD0600404] and Natural Science Foundation of China [grant number 41871278].

语种：英文

外文关键词：Tree segmentation; airborne LiDAR; spectral clustering; Nystrom approximation; sampling method

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 Nystrom-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 Nystrom 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.