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Deep Gaussian Processes for geophysical parameter retrieval
December 07, 2020 Β· Declared Dead Β· π IEEE International Geoscience and Remote Sensing Symposium
"No code URL or promise found in abstract"
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Authors
Daniel Heestermans Svendsen, Pablo Morales-Γlvarez, Rafael Molina, Gustau Camps-Valls
arXiv ID
2012.12099
Category
physics.geo-ph
Cross-listed
cs.LG,
eess.SP,
stat.AP
Citations
4
Venue
IEEE International Geoscience and Remote Sensing Symposium
Last Checked
3 months ago
Abstract
This paper introduces deep Gaussian processes (DGPs) for geophysical parameter retrieval. Unlike the standard full GP model, the DGP accounts for complicated (modular, hierarchical) processes, provides an efficient solution that scales well to large datasets, and improves prediction accuracy over standard full and sparse GP models. We give empirical evidence of performance for estimation of surface dew point temperature from infrared sounding data.
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