Identification of Surface Deformation in InSAR Using Machine Learning
Author:
Affiliation:
1. Department of Earth and Environmental Sciences University of Iowa Iowa City IA USA
2. Now at U.S. Geological Survey Earthquake Hazards Program Office Golden CO USA
Funder
National Aeronautics and Space Administration
Publisher
American Geophysical Union (AGU)
Subject
Geochemistry and Petrology,Geophysics
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020GC009204
Reference46 articles.
1. Estimating soil moisture using remote sensing data: A machine learning approach
2. Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data
3. A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets
4. The Application of Convolutional Neural Networks to Detect Slow, Sustained Deformation in InSAR Time Series
5. Application of Machine Learning to Classification of Volcanic Deformation in Routinely Generated InSAR Data
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