3-D terrain from synthetic aperture radar images

Surface analysis is important for automatic terrain cartography, and for airborne navigation. This paper proposes a new approach to shape-from-shading (SFS) in synthetic aperture radar (SAR) images. The SFS problem is embedded in a Bayesian framework. We maximize the surface orientation probability using SAR image statistics, local smoothing and constraints imposed by object discontinuities. We model the statistics of the SAR image distribution as a product between the Rayleigh and Bessel functions. We derive the optimal edge detector for this distribution. The resulting edges are classified as ridges and ravines according to a statistical test. Afterwards, the edges are used as constraints in the estimation of the surface normals. We propose various smoothing algorithms for the vector field of surface normals using robust statistics and surface curvature consistency. The results provided by these algorithms are compared with those given by local averaging.