Disparity estimation for high resolution stereoscopic reconstruction using the GNC approach

High resolution digital elevation model (DEM) are used in many applications, including topographic description of the ground (urban, telecommunications...). We consider the derivation of DEMs from two rectified stereoscopic images. This problem can be formulated as a large scale non-convex optimization problem under constraints, where the unknown is a disparity image and the cost function includes sum of correlation coefficients and a regularization term. We propose two optimization schemes: one deterministic and mainly inspired by the graduated non-convexity (GNC) technique proposed by Blake and Zisserman (1987); the other relies on simulated annealing (SA) and can be related to the method proposed by Barnard (1989). We compare the resulting algorithms and show some results on urban area aerial images