Motion estimation of 2D atmospheric layers from satellite image sequences

In this paper, we address the problem of estimating mesoscale dynamics of atmospheric layers from satellite image sequences. Due to the great deal of spatial and temporal distortions of cloud patterns and because of the sparse three-dimensional nature of cloud observations, standard dense motion field estimation techniques used in computer vision are not well adapted to satellite images. Relying on a physically sound vertical decomposition of the atmosphere into layers of different pressure interval, 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 mesoscale observations related to each layer. A novel spatio-temporal regularizer derived from large eddy prediction of shallow-water momentum conservation model is used to build constraints for large scale temporal coherence. These constraints are combined in a global smoothing framework with a robust second-order regularizer preserving divergent and vorticity structures of the flow. For optimization, a twostage motion estimation scheme is proposed to overcome multiresolution limitations when capturing the dynamics of mesoscale structures. This alternative approach relies on the combination of correlation and optical-flow observations in a variational context. The motion estimation method is assessed on a Météosat image sequence.