Aerial and Satellite Image Segmentation Based on Shape- Constrained Geodesic Active Contours

In this paper, we propose a novel mathematical model to address segmentation from an aerial and satellite imagery. Segmentation is performed through the consistent recovery of the zero iso-surfaces of a level set function towards image’s foreground (desired object for extraction) and background discrimination. The level set method is embedded in a geodesic active contours variational formulation. Geodesic active contours technique is an advancement of the classical snakes and active contours that can handle the limitation to change their topology. The proposed mathematical model consists of a functional with two basic energies; one that forms a region-based energetic module for the evolving interface in the level set space and an another one that takes into account the a priori knowledge of the geometry of the desired for extraction objects. With such a functional shape information is embedded into the level set based segmentation scheme. Shape information can cope with missing or misleading information in the input images due to noise, clutter and occlusion. The shape priors were developed to segment objects of familiar shape in a given image. Promising results demonstrate the potentials of our approach, like in cases where the desired object for extraction were buildings.