Bayesian Estimation of Surface Information from Radar Images

Abstract

The dissertation presents a method for deriving the shape of a surface from a radar image of the surface. An appropriate model of radar image formation is derived from physical principles. A Bayesian formulation of the inversion problem is developed upon which a computational strategy is based. Theoretical results on random surfaces relevant to the prior distribution are presented, and convergence and optimality properties of a new sampling algorithm are described. The technique is applied to Magellan data of Venus