Individual Tree Extraction from Terrestrial LiDAR Point Clouds Based on Transfer Learning and Gaussian Mixture Model Separation
Individual Tree Extraction from Terrestrial LiDAR Point Clouds Based on Transfer Learning and Gaussian Mixture Model Separation
复制标题
基于迁移学习和高斯混合模型分离的地面激光雷达点云单树提取
DOI：
10.3390/rs13020223
复制
发表时间：
2021-01
期刊：
影响因子：
5
通讯作者：
Liu Bo
中科院分区：
文献类型：
--
作者：
Hui Zhenyang;Jin Shuanggen;Li Dajun;Yevenyo Yao Ziggah;Liu Bo
Individual tree extraction is an important process for forest resource surveying and monitoring. To obtain more accurate individual tree extraction results, this paper proposed an individual tree extraction method based on transfer learning and Gaussian mixture model separation. In this study, transfer learning is first adopted in classifying trunk points, which can be used as clustering centers for tree initial segmentation. Subsequently, principal component analysis (PCA) transformation and kernel density estimation are proposed to determine the number of mixed components in the initial segmentation. Based on the number of mixed components, the Gaussian mixture model separation is proposed to separate canopies for each individual tree. Finally, the trunk stems corresponding to each canopy are extracted based on the vertical continuity principle. Six tree plots with different forest environments were used to test the performance of the proposed method. Experimental results show that the proposed method can achieve 87.68% average correctness, which is much higher than that of other two classical methods. In terms of completeness and mean accuracy, the proposed method also outperforms the other two methods.
登录
查看更多内容
根据 TLS 和 MLS 数据分割单个树
DOI：
10.1109/jstars.2016.2565519
发表时间：
2017
影响因子：
5.5
作者：
Zhong Lishan;Cheng Liang;Xu Hao;Wu Yang;Chen Yanming;Li Manchun
通讯作者：
Li Manchun
DOI：
10.3390/rs70201877
发表时间：
2015-02
期刊：
Remote. Sens.
影响因子：
--
作者：
Shruthi Srinivasan;S. Popescu;Marian Eriksson;R. Sheridan;N. Ku
通讯作者：
Shruthi Srinivasan;S. Popescu;Marian Eriksson;R. Sheridan;N. Ku
影响因子：
2.9
作者：
W. M. Jaafar;I. Woodhouse;C. Silva;H. Omar;K. N. A. Maulud;A. Hudak;Carine Klauberg;A. Cardil;M. Mohan
通讯作者：
W. M. Jaafar;I. Woodhouse;C. Silva;H. Omar;K. N. A. Maulud;A. Hudak;Carine Klauberg;A. Cardil;M. Mohan
DOI：
10.1109/jstars.2016.2541780
发表时间：
2016-04
影响因子：
5.5
作者：
W. Xiao;Sudan Xu;S. O. Elberink;G. Vosselman
通讯作者：
W. Xiao;Sudan Xu;S. O. Elberink;G. Vosselman
DOI：
10.1016/j.isprsjprs.2012.10.003
发表时间：
2013-01-01
影响因子：
12.7
作者：
Kankare, Ville;Holopainen, Markus;Alho, Petteri
通讯作者：
Alho, Petteri