Abstract
In this paper, we address the problem of estimating mesoscale dynamics of atmospheric layers from satellite image sequences. Relying on a physically sound vertical decomposition of the atmosphere into layers, we propose a dense motion estimator dedicated to the extraction of multi-layer horizontal wind fields. This estimator is expressed as the minimization of a global function including a data term and a spatio-temporal smoothness term. A robust data term relying on shallow-water mass conservation model is proposed to fit sparse observations related to each layer. A novel spatio-temporal regularizer derived from shallow-water momentum conservation model is proposed to enforce a temporal consistency of the solution along the sequence time range. These constraints are combined with a robust second-order regularizer preserving divergent and vorticity structures of the flow. In addition, a two-level motion estimation scheme is proposed to overcome the limitations of the multiresolution incremental scheme when capturing the dynamics of fine mesoscale structures. This alternative approach relies on the combination of correlation and optical-flow observations. An exhaustive evaluation of the novel method is first performed on a scalar image sequence generated by Direct Numerical Simulation of a turbulent bi-dimensional flow and then on a Meteosat infrared image sequence.
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Héas, P., Mémin, É., Papadakis, N. (2007). A Consistent Spatio-temporal Motion Estimator for Atmospheric Layers. In: Sgallari, F., Murli, A., Paragios, N. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2007. Lecture Notes in Computer Science, vol 4485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72823-8_22
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DOI: https://doi.org/10.1007/978-3-540-72823-8_22
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