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The complexity of dynamical laws governing 3D atmospheric flows associated with incomplete and noisy observations make the recovery of atmospheric dynamics from satellite images sequences very difficult. In this paper, we face the challenging problem of estimating physical sound and time-consistent horizontal motion fields at various atmospheric depths for a whole image sequence. Based on a vertical...
In this paper, we present a framework for dynamic consistent estimation of dense motion fields over a sequence of images. The originality of the approach is to exploit recipes related to optimal control theory. This setup allows performing the estimation of an unknown state function according to a given dynamical model and to noisy and incomplete measurements. The overall process is formalized through...
In this paper, we present a framework for dynamic consistent estimation of dense motion fields over a sequence of Meteosat Second Generation (MSG) images. The originality of the approach is to exploit recipes related to optimal control theory developed in geophysical sciences. This framework, known as variational data assimilation, enables to perform the estimation of an unknown state function according...
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