Joint road network extraction from a set of high resolution satellite images

In this paper, we develop a novel Conditional Random Field (CRF) formulation to jointly extract road networks from a set of high resolution satellite images. Our fully unsupervised method relies on a pairwise CRF model defined over a set of test images, which encodes prior assumptions about the roads such as thinness, elongation. Four competitive energy terms related to color, shape, symmetry and contrast-sensitive potentials are suitably defined to tackle with the challenging problem of road network extraction. The resulting objective energy is minimized by resorting to graph-cuts tools. Promising results are obtained for developed suburban scenes in remotely sensed images. The proposed model improve significantly the segmentation quality, compared against the independent CRF and two state-of-the-art methods.