Latent force models for earth observation time series prediction

Luengo García, David and Campos-Taberner, Manuel and Camps-Valls, Gustau (2016). Latent force models for earth observation time series prediction. In: "MLSP2016 IEEE International Workshop on Machine Learning for Signal Processing", 13/09/2016 - 16/09/2016, Salerno (Italia). ISBN 978-1-5090-0746-2. pp. 1-6. https://doi.org/10.1109/MLSP.2016.7738844.

Description

Title: Latent force models for earth observation time series prediction
Author/s:
  • Luengo García, David
  • Campos-Taberner, Manuel
  • Camps-Valls, Gustau
Item Type: Presentation at Congress or Conference (Article)
Event Title: MLSP2016 IEEE International Workshop on Machine Learning for Signal Processing
Event Dates: 13/09/2016 - 16/09/2016
Event Location: Salerno (Italia)
Title of Book: Proceedings of the 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)
Date: 2016
ISBN: 978-1-5090-0746-2
Subjects:
Freetext Keywords: Latent force models, Gaussian processes, Remote sensing, satellite images, Moderate Resolution Imaging Spectroradiometer (MODIS), gap filling, multi-output regression, rice crop monitoring.
Faculty: E.T.S.I. y Sistemas de Telecomunicación (UPM)
Department: Teoría de la Señal y Comunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

We introduce latent force models for Earth observation time series analysis. The model uses Gaussian processes and differential equations to combine data driven modelling with a physical model of the system. The LFM presented here performs multi-output structured regression, adapts to the signal characteristics, it can cope with missing data in the time series, and provides explicit latent functions that allow system analysis and evaluation. We successfully illustrate the performance in challenging scenarios of crop monitoring from space, providing time-resolved time series predictions.

Funding Projects

TypeCodeAcronymLeaderTitle
FP7FP7/2007-2013UnspecifiedUnspecifiedUnspecified
Horizon 2020ERC-CoG-2014SEDALUnspecifiedUnspecified
Government of SpainTEC2015-64835-C3-3-RUnspecifiedUnspecifiedUnspecified
Government of SpainTIN2015-64210-RUnspecifiedUnspecifiedUnspecified

More information

Item ID: 46523
DC Identifier: http://oa.upm.es/46523/
OAI Identifier: oai:oa.upm.es:46523
DOI: 10.1109/MLSP.2016.7738844
Official URL: http://mlsp2016.conwiz.dk/home.htm
Deposited by: Memoria Investigacion
Deposited on: 24 Apr 2018 18:08
Last Modified: 24 Apr 2018 18:08
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