A Markovian Approach for DEM Estimation From Multiple InSAR Data With Atmospheric Contributions

Accurate digital elevation model (DEM) estimation using synthetic aperture radar interferometry still remains a challenging problem in the geographical information science community, particularly in dealing with a high noise rate and atmospheric disturbances. Such task suffers from the lack of efficient and reliable methods to overcome these artifacts. This work provides a method that aims to solve this problem through a Bayesian formulation with the Markovian energy minimization framework. The DEM is generated from a set of multifrequency/multibaseline interferograms using a multichannel phase unwrapping algorithm combined with an estimation method of the atmospheric artifacts. A set of experimental results illustrates the effectiveness and robustness of the proposed approach.