DeepEmSat: Deep Emulation for Satellite Data Mining

The growing volume of Earth science data available from climate simulations and satellite remote sensing offers unprecedented opportunity for scientific insight, while also presenting computational challenges. One potential area of impact is atmospheric correction, where physics-based numerical mode...

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Bibliographic Details
Published in:Frontiers in Big Data
Main Authors: Kate Duffy, Thomas Vandal, Shuang Li, Sangram Ganguly, Ramakrishna Nemani, Auroop R. Ganguly
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-12-01
Subjects:
remote sensing
machine learning
deep learning
atmospheric correction
emulator
Online Access:https://www.frontiersin.org/article/10.3389/fdata.2019.00042/full

Internet

https://www.frontiersin.org/article/10.3389/fdata.2019.00042/full

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