Understanding and forecasting the evolution of geophysical fluids is a major scientific and societal challenge. Forecasting algorithms should take into account all the available informations on the considered dynamical system. The Variational Data Assimilation (VDA) technique combines in a consistent way all these informations in an Optimality System in order to reconstruct the model inputs. VDA is currently used by the major meteorological centres. During the last two decades about thirty satellites were launched to improve the knowledge of the atmosphere and of the oceans. They continuously provide a huge amount of data that are still underused by numerical forecast systems. In particular, the dynamical evolution of some meteorological or oceanic features (such as eddies, fronts, \dots) that a human vision may easily detect is not optimally taken into account in realistic applications of VDA. Image Assimilation in VDA framework can be performed using \textit{pseudo-observation} techniques : they provide some apparent velocity fields which are assimilated as classical observations. These measurements are obtained by some external procedures which are decoupled with the considered dynamical system. In this paper, we suggest a more consistent approach which directly incorporates image sequences into the Optimality System.