Deep Gaussian Processes for geophysical parameter retrieval

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.

Preprint, Paper published in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, 2018, pp. 6175-6178

References in corpus (2)

• Deep Gaussian Processes
• Remote Sensing Image Classification with Large Scale Gaussian Processes

Cited by in corpus (1)

• Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion