Generative Feature Extraction From Sentinel 1 and 2 Data for Prediction of Forest Aboveground Biomass in the Italian Alps
Author:
Affiliation:
1. Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
2. Research and Innovation Center, Fondazione Edmund Mach, San Michele all'Adige, Italy
Funder
European Commission
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/9656571/09785720.pdf?arnumber=9785720
Reference76 articles.
1. An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products
2. Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery
3. Estimating the Aboveground Biomass of an Evergreen Broadleaf Forest in Xuan Lien Nature Reserve, Thanh Hoa, Vietnam, Using SPOT-6 Data and the Random Forest Algorithm
4. PolSAR tools: A QGIS plugin for generating SAR descriptors
5. Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN
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