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Analyse massive d'images multi-angulaires hyperspectrales de la planète Mars par régression inverse de modèles physiques

Abstract : The objective of the thesis is to develop a statistical learning technique suitable for the inversion of complex physical models. The two main difficulties addressed are on the one hand the massive amount of observations to be reversed and on the other hand the need to quantify the uncertainty on the inversion, which can come from the physical model or from the measurements. In a Bayesian inversion framework, we therefore propose a two-step approach: a learning step of a parametric statistical model (GLLiM), common to all the observations, then a prediction step, repeated for each measurement, but fast enough to support a large dataset. In addition, we explore sampling techniques to refine the trade-off between computation time and inversion precision.Although general, the proposed approach is applied mainly to a complex inverse problem in planetary remote sensing. This involves using a semi-empirical spectrophotometric model (Hapke's model) to analyze reflectance measurements and indirectly find the textural characterization of the material examined. Several datasets are studied, both from laboratory measurements and massive satellite images.Finally, we exploit the versatility of the GLLiM model to explore several issues related to Bayesian inversion. In particular, we propose an indicator to assess the influence of the choice of the direct model on the quality of the inversion. We also extend the GLLiM model to take into account a priori information, making it suitable for solving data assimilation problems.
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Submitted on : Friday, November 5, 2021 - 3:31:13 PM
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  • HAL Id : tel-03417184, version 1

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Benoit Kugler. Analyse massive d'images multi-angulaires hyperspectrales de la planète Mars par régression inverse de modèles physiques. Traitement des images [eess.IV]. Université Grenoble Alpes [2020-..], 2021. Français. ⟨NNT : 2021GRALM021⟩. ⟨tel-03417184⟩

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