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 2019-12, Vol.2, p.42-42
Main Authors: Duffy, Kate, Vandal, Thomas, Li, Shuang, Ganguly, Sangram, Nemani, Ramakrishna, Ganguly, Auroop R
Format: Article
Language:English
Subjects:
atmospheric correction
Big Data
deep learning
emulator
machine learning
remote sensing
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Summary: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 models retrieve surface reflectance information from top of atmosphere observations, and are too computationally intensive to be run in real time. Machine learning methods have demonstrated potential as fast statistical models for expensive simulations and for extracting credible insights from complex datasets. Here, we develop DeepEmSat: a deep learning emulator approach for atmospheric correction, and offer comparison against physics-based models to support the hypothesis that deep learning can make a contribution to the efficient processing of satellite images.
ISSN:2624-909X
2624-909X
DOI:10.3389/fdata.2019.00042