Automated tree-crown and height detection in a young forest plantation using mask region-based convolutional neural network (Mask R-CNN)-Reference-Cited by-同舟云学术

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Automated tree-crown and height detection in a young forest plantation using mask region-based convolutional neural network (Mask R-CNN)

Published:2021-08 Issue: Volume:178 Page:112-123
ISSN:0924-2716
Container-title:ISPRS Journal of Photogrammetry and Remote Sensing
language:en
Short-container-title:ISPRS Journal of Photogrammetry and Remote Sensing

Author:

Hao Zhenbang,Lin Lili,Post Christopher J.,Mikhailova Elena A.,Li Minghui,Chen Yan,Yu Kunyong,Liu Jian

Funder

China Scholarship Council

Fujian Provincial Department of Science and Technology

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Computer Science Applications,Engineering (miscellaneous),Atomic and Molecular Physics, and Optics

Reference70 articles.

1. Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar;Alonzo;Remote Sens. Environ.,2020

2. Braga, J.R., Peripato, V., Dalagnol, R., P. Ferreira, M., Tarabalka, Y., O. C. Aragão, L.E., F. De Campos Velho, H., Shiguemori, E.H. and Wagner, F.H., 2020. Tree crown delineation algorithm based on a convolutional neural network. Remote sensing, 12(8): 1288.

3. An unexpectedly large count of trees in the West African Sahara and Sahel;Brandt;Nature,2020

4. Estimating individual conifer seedling height using drone-based image point clouds;Castilla;Forests,2020

5. Automatic delineation and height measurement of regenerating conifer crowns under leaf-off conditions using UAV Imagery;Chadwick;Remote Sens.,2020

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