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Based on physical laws describing the multiscale structure of turbulent flows, this paper proposes a regularizer for fluid motion estimation from an image sequence. Regularization is achieved by imposing some scale invariance property between histograms of motion increments computed at different scales. By reformulating this problem from a Bayesian perspective, an algorithm is proposed to jointly estimate motion, regularization hyperparameters, and to select the most likely physical prior among a set of models. Hyperparameter and model inference are conducted by posterior maximization, obtained by marginalizing out non--Gaussian motion variables. The Bayesian estimator is assessed on several image sequences depicting synthetic and real turbulent fluid flows. Results obtained with the proposed approach exceed the state-of-the-art results in fluid flow estimation. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of estimating 3-D motions of a stratified atmosphere from satellite image sequences. The analysis of 3-D 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 image 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 3-D wind fields characterizing the dynamics of a layered atmosphere. Wind estimation is performed over the complete 3-D space, using a multilayer 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. [ABSTRACT FROM AUTHOR]

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 3-D 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 multilayer horizontal wind fields. This estimator is expressed as the minimization of a global function including data and spatio-temporal smoothness terms. 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 a 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 2-D flow. By qualitative comparisons, the method is then assessed on a METEOSAT image sequence. [ABSTRACT FROM AUTHOR]

During the last decades, satellites have acquired incessantly high-resolution images of many Earth observation sites. New products have arisen from this intensive acquisition process: high-resolution satellite image time-series (SITS). They represent a large data volume with a rich information content and may open a broad range of new applications. This paper presents an information mining concept which enables a user to learn and retrieve spatio-temporal structures in SITS. The concept is based on a hierarchical Bayesian modeling of SITS information content which enables us to link the interest of a user to specific spatio-temporal structures. The hierarchy is composed of two inference steps: an unsupervised modeling of dynamic clusters resulting in a graph of trajectories, and an interactive learning procedure based on graphs which leads to the semantic labeling of spatio-temporal structures. Experiments performed on a SPOT image time-series demonstrate the concept capabilities. [ABSTRACT FROM AUTHOR]