Recognition of Forest Damage from Sentinel-2 Satellite Images Using U-Net, RandomForest and XGBoost-Reference-Cited by-同舟云学术

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Recognition of Forest Damage from Sentinel-2 Satellite Images Using U-Net, RandomForest and XGBoost

Published:2024-02-29 Issue: Volume:7 Page:1-6
ISSN:
Container-title:2024 6th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)
language:
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Author:

Podoprigorova N.S.¹,Safonov F.A.¹,Podoprigorova S.S.¹,Tarasov A.V.²,Shikhov A.N.²

Affiliation:

1. Bauman Moscow State Technical University,Moscow,Russian Federation

2. Perm State National Research University,Perm,Russian Federation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10479558/10479559/10479810.pdf?arnumber=10479810

Reference27 articles.

1. Hyperspectral Remote Sensing Images Classification Using Fully Convolutional Neural Network

2. Novel Land Cover Change Detection Method Based on k-Means Clustering and Adaptive Majority Voting Using Bitemporal Remote Sensing Images

3. Analysing detection bands of two-spectral reflection method to identify forest species composition

4. Vegetation Land Use/Land Cover Extraction From High-Resolution Satellite Images Based on Adaptive Context Inference

5. Multispectral Optical Reflectometry Method of Forest Resource Monitoring

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