Deep Gaussian Processes for geophysical parameter retrieval

Daniel Heestermans Svendsen; Pablo Morales-Álvarez; Rafael Molina; Gustau Camps-Valls

doi:10.1109/igarss.2018.8517647

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arXiv · 2012.12099

Deep Gaussian Processes for geophysical parameter retrieval

Daniel Heestermans Svendsen·Pablo Morales-Álvarez·Rafael Molina·Gustau Camps-Valls

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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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Daniel Heestermans Svendsen, Pablo Morales-Álvarez, Rafael Molina, Gustau Camps-Valls. 2020-12-07. Deep Gaussian Processes for geophysical parameter retrieval. https://doi.org/10.1109/igarss.2018.8517647

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