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Memorias de investigación
Ponencias en congresos:
Physics-Aware Gaussian Processes for Earth Observation
Áreas de investigación
  • Teoría de probabilidades y procesos estocásticos,
  • Estadística,
  • Ciencias de la tierra
Earth observation from satellite sensory data pose challenging problems, where machine learning is currently a key player. In recent years, Gaussian Process (GP) regression and other kernel methods have excelled in biophysical parameter estimation tasks from space. GP regression is based on solid Bayesian statistics, and generally yield efficient and accurate parameter estimates. However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only. Very often a forward model encoding the well-understood physical relations is available though. In this work, we review three GP models that respect and learn the physics of the underlying processes in the context of inverse modeling. First, we will introduce a Joint GP (JGP) model that combines in situ measurements and simulated data in a single GP model. Second, we present a latent force model (LFM) for GP modeling that encodes ordinary differential equations to blend data-driven modeling and physical models of the system. The LFM performs multi-output regression, adapts to the signal characteristics, is able to cope with missing data in the time series, and provides explicit latent functions that allow system analysis and evaluation. Finally, we present an Automatic Gaussian Process Emulator (AGAPE) that approximates the forward physical model via interpolation, reducing the number of necessary nodes. Empirical evidence of the performance of these models will be presented through illustrative examples of vegetation monitoring and atmospheric modeling.
Nombre congreso
Scandinavian Conference on Image Analysis (SCIA)
Tipo de participación
Lugar del congreso
Tromso (Noruega)
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Título de las actas
Proceedings of the Scandinavian Conference on Image Analysis (SCIA)
Esta actividad pertenece a memorias de investigación
  • Autor: David Luengo Garcia (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Teoría de Aproximación Constructiva y Aplicaciones
  • Departamento: Teoría de la Señal y Comunicaciones (Provisional)
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