In order to forecast the evolution of a dynamical system such as géophisical fluids (ocean, atmosphere, continental waters), all the available information have to be accounted for. They are of very different nature : set of non linear PDE (mathematical-type information), in situ measurements and remote sensing (physical-type information), statistical and qualitative informations. The forecast is produced through a model integration starting from an initial state, from which the system evolution is very sensitive. Consequently, the issue is to evaluate the initial state in a consistent manner from all this heterogeneous sources of information. At the beginning of the 80s, techniques coming from the optimal control theory were proposed to achieve this task. These techniques are now adopted by the main numerical weather forecast centres. For few decades, a large number satellites dedicated to earth observation has been launched, in order the improve our knowledge of the atmosphere and the oceans. They provide, among other things, numerous sequences of images. These sequences clearly have a strong predictive potential due to the fact that they contain information about the dynamics of the observed system. Currently, this kind of information is unfortunately not used in an optimal manner in conjunction with the numerical models. This poster presents an extension of the optimal control based techniques to the assimilation of images. A quadratic term measuring the misfit between the images equivalent produced by the model and the observed images is introduced in the usual cost function.