6533b873fe1ef96bd12d4e1a
RESEARCH PRODUCT
Learning Structures in Earth Observation Data with Gaussian Processes
Jochem VerrelstJordi Muñoz-maríFernando MateoGustau Camps-vallsValero Laparrasubject
FOS: Computer and information sciencesEarth observation010504 meteorology & atmospheric sciencesComputer science0211 other engineering and technologiesFOS: Physical sciencesMachine Learning (stat.ML)02 engineering and technologyApplied Physics (physics.app-ph)computer.software_genre01 natural sciencesField (computer science)Physics::GeophysicsSet (abstract data type)Physics - Geophysicssymbols.namesakeStatistics - Machine LearningFeature (machine learning)Gaussian process021101 geological & geomatics engineering0105 earth and related environmental sciencesbusiness.industryPhysics - Applied PhysicsGeophysics (physics.geo-ph)Function approximationsymbolsGlobal Positioning SystemNoise (video)Data miningbusinesscomputerdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-12-22 |