Recovering missing data on satellite images

Isabelle Herlin (1, 2) , Dominique Béréziat (3) , Nicolas Mercier (1, 2)

1 Clime - Coupling environmental data and simulation models for software integration
2 CEREA - Centre d'Enseignement et de Recherche en Environnement Atmosphérique
3 PEQUAN - Performance et Qualité des Algorithmes Numériques

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.

Domaines: Modélisation et simulation

Isabelle Herlin, Dominique Béréziat, Nicolas Mercier. Recovering missing data on satellite images. SCIA 2011 - Scandinavian Conference on Image Analysis, May 2011, Ystad Saltsjöbad, Sweden. pp.697-707, ⟨10.1007/978-3-642-21227-7_65⟩. ⟨inria-00612328⟩