Deeply supervised network for airborne LiDAR tree classification incorporating dual attention mechanisms-Reference-Cited by-同舟云学术

Deeply supervised network for airborne LiDAR tree classification incorporating dual attention mechanisms

Published:2024-01-18 Issue:1 Volume:61 Page:
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Zhang Zhenyu¹,Wang Jian¹²,Wu Yunze³,Zhao Youlong¹,Wu Binjie⁴

Affiliation:

1. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China

2. Qingdao Key Laboratory of Beidou Navigation and Intelligent Spatial Information Technology Application, Shandong University of Science and Technology, Qingdao, China

3. College of Surveying and Geo-Informatics, Tongji University, Shanghai, China

4. Operations Management Department, Xilingol Mengshan Gold Alhada Mining Co. Ltd, Inner Mongolia, China

Funder

the Natural Science Foundation of Shandong Province, Research on Key Technology of Urban Underground Space Safety Monitoring Based on LiDAR Technology

Tenth Five-Year Plan

Shandong University of Science and Technology College Students Innovation and Entrepreneurship Training Program

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2024.2303866

Reference54 articles.

1. Tree species identification in mixed coniferous forest using airborne laser scanning

2. Exploring Multispectral ALS Data for Tree Species Classification

3. Tree Species Structure, Composition, and Diversity in Sierra Leone Forest Ecosystem: An Evaluation of Two Protected Forests;Banya J.;Applied Ecology and Environmental Sciences,2021

4. SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection

5. CLASSIFICATION OF TREE SPECIES AND STANDING DEAD TREES BY FUSING UAV-BASED LIDAR DATA AND MULTISPECTRAL IMAGERY IN THE 3D DEEP NEURAL NETWORK POINTNET++

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Adaptive fusion of different platform point cloud with improved particle swarm optimization and supervoxels;International Journal of Applied Earth Observation and Geoinformation;2024-06