Three-Dimensional Motion Estimation of Atmospheric Layers From Image Sequences

Abstract : In this paper, we address the problem of estimating three-dimensional motions of a stratified atmosphere from satellite image sequences. The analysis of three-dimensional atmospheric fluid flows associated with incomplete observation of atmospheric layers due to the sparsity of cloud systems is very difficult. This makes the estimation of dense atmospheric motion field from satellite images sequences very difficult. The recovery of the vertical component of fluid motion from a monocular sequence of image observations is a very challenging problem for which no solution exists in the literature. Based on a physically sound vertical decomposition of the atmosphere into cloud layers of different altitudes, we propose here a dense motion estimator dedicated to the extraction of three-dimensional wind fields characterizing the dynamics of a layered atmosphere. Wind estimation is performed over the complete three-dimensional space using a multi-layer model describing a stack of dynamic horizontal layers of evolving thickness, interacting at their boundaries via vertical winds. The efficiency of our approach is demonstrated on synthetic and real sequences.
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IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2008, 46 (8), pp.2385-2396. 〈10.1109/TGRS.2008.918167〉
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Patrick Héas, Etienne Memin. Three-Dimensional Motion Estimation of Atmospheric Layers From Image Sequences. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2008, 46 (8), pp.2385-2396. 〈10.1109/TGRS.2008.918167〉. 〈hal-00670348〉

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