Comparison of Deep and Machine Learning Approaches for Quebec Tree Species Classification Using a Combination of Multispectral and LiDAR Data-Reference-Cited by-同舟云学术

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Comparison of Deep and Machine Learning Approaches for Quebec Tree Species Classification Using a Combination of Multispectral and LiDAR Data

Published:2024-06-11 Issue:1 Volume:50 Page:
ISSN:0703-8992
Container-title:Canadian Journal of Remote Sensing
language:en
Short-container-title:Canadian Journal of Remote Sensing

Author:

Reisi Gahrouei Omid¹,Côté Jean-François²,Bournival Philippe³,Giguère Philippe⁴,Béland Martin¹

Affiliation:

1. Digital Forest Lab, Department of Geomatics Sciences, Laval University, Quebec City, QCG1V 0A6, Canada

2. Canadian Wood Fibre Centre, Canadian Forest Service, Natural Resources Canada, Québec City, QCG1V 4C7, Canada

3. Ministry of Natural Resources and Forests, 5700, 4ième Avenue Ouest, Québec City, QCG1H 6R1, Canada

4. Department of Computer Science and Software Engineering, Université Laval, Québec City, QCG1V 0A6, Canada

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/07038992.2024.2359433

Reference63 articles.

1. Large-Scale Classification of Urban Structural Units From Remote Sensing Imagery

2. Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers

3. On promoting the use of lidar systems in forest ecosystem research

4. Mapping tree species proportions from satellite imagery using spectral–spatial deep learning

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