A Consistent Spatio-temporal Motion Estimator for Atmospheric Layers

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 spatiotemporal 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 shallowwater 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 i...

• Type: Conference
• Year: 2007
• Where: SCALESPACE
• Authors: Patrick Héas, Étienne Mémin, Nicolas Papadakis
• Keywords: Data Term, Dense Motion Estimator, Image Sequence, SCALESPACE 2007