Recovering Missing Data on Satellite Images

  • Isabelle Herlin
  • Dominique Béréziat
  • Nicolas Mercier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)

Abstract

Data Assimilation is commonly used in environmental sciences to improve forecasts, obtained by meteorological, oceanographic or air quality simulation models, with observation data. It aims to solve an evolution equation, describing the dynamics, and an observation equation, measuring the misfit between the state vector and the observations, to get a better knowledge of the actual system’s state, named the reference. In this article, we describe how to use this technique to recover missing data and reduce noise on satellite images. The recovering process is based on assumptions on the underlying dynamics displayed by the sequence of images. This is a promising alternative to methods such as space-time interpolation. In order to better evaluate our approach, results are first quantified for an artificial noise applied on the acquisitions and then displayed for real data.

Keywords

State Vector Satellite Image Data Assimilation Adjoint Variable Variational Data Assimilation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Isabelle Herlin
    • 1
    • 2
  • Dominique Béréziat
    • 3
  • Nicolas Mercier
    • 1
    • 2
  1. 1.INRIAFrance
  2. 2.CEREA, Join Laboratory ENPC–EDF R&DUniversité Paris-EstFrance
  3. 3.Université Pierre et Marie Curie – LIP6France

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