A Level-Set-Based Image Assimilation Method: Potential Applications for Predicting the Movement of Oil Spills

In this paper, we present a novel method for assimilating geometric information from observed images. Image assimilation technology fully utilizes structural information from the dynamics of the images to retrieve the state of a system and thus to better predict its evolution. Level-set method describing the evolution of the geometry shapes of a given system is taken into account to include the dynamics of the images. This method takes advantage of Lagrangian information in an Eulerian numerical framework. In our numerical experiments, we apply this state-of-the-art technique to a pollutant transport problem, to calibrate the initial contours of pollutants and to identify diffusion coefficients of the model. It can be shown a potential approach for oil spills, because topological merging and breaking of oil slicks are well defined and easily performed by this proposed approach. Numerical results show that the proposed method is visibly efficient compared with the classical method based on the concentration map when the concentration measurements and the background fields are not well available.