Gaussian Processes (GPs) has experienced tremendous success in geoscience in general and for bio-geophysical parameter retrieval in the last years. GPs constitute a solid Bayesian framework to formulate many function approximation problems consistently. This paper reviews the main theoretical GP developments in the field. We review new algorithms that respect the signal and noise characteristics, that provide feature rankings automatically, and that allow applicability of associated uncertainty intervals to transport GP models in space and time. All these developments are illustrated in the field of geoscience and remote sensing at a local and global scales through a set of illustrative examples.
CITATION STYLE
Mateo, F., Muñoz-Marí, J., Laparra, V., Verrelst, J., & Camps-Valls, G. (2016). Learning structures in earth observation data with gaussian processes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9785 LNCS, pp. 78–94). Springer Verlag. https://doi.org/10.1007/978-3-319-44412-3_6
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