In this work, 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 3-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, 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 the integrated continuity equation mass conservation model is proposed to fit sparse transmittance observations related to each layer. A novel spatio-temporal smoother 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 smoother preserving divergent and vorticity structures of the flow. For optimization, a two-stage 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. An exhaustive evaluation of the novel method is first performed on a scalar image sequence generated by direct numerical simulation of a turbulent twodimensional flow. By qualitative comparisons, the method is then assessed on a METEOSAT image sequence.

References:

D. Heitz, P. Héas, E. Mémin, J. Carlier. Dynamic consistent correlation-variational approach for robust optical flow estimation. Experiments in Fluids. 45(4):595-608, 2008.

P. Héas, E. Mémin, N. Papadakis, A. Szantai. Layered estimation of atmospheric mesoscale dynamics from satellite imagery . IEEE transactions on Geosciences and Remote Sensing, pp. 4087-4104, vol. 45, Issue 12, Part 2, 2007

Software:

1- P. Héas, E. Mémin, N. Papadakis. Layers motion estimation methods (with physical coupling between layers), Fluid Project deliverable 4.1a, 2007 ."download"

2- P. Héas, E. Mémin, N. Papadakis. Demonstrator on multigrid multiscale motion estimation , Fluid Project deliverable 2.4, 2007 ."download"