Motion study in computer vision highly depends on the type of imagery and on the kind of the studied objects. A popular approach of non-rigid motion supposes that the objects are continuously and locally deformed. Local features, such as curvature extrema, are then often used. However, these features can not always be accurately computed, and motion may involve large deformation of the object between two consecutive temporal occurrences. In these cases, a real need exists for an approach that does not rely on local features. That study of motion requires additional information. We introduce a geometrical evolution model that enables one to generate a surface interpolating successive contours of the object during its temporal evolution. This geometrical model may be viewed as a simplification of a true physical model of motion. This approach is particularly well suited to remote sensed data: structures of interest do not have a well defined shape, and the temporal resolution may be poor, involving large deformation. The model is successfully applied to vortex tracking on sea color and meteorologic images