Compressed sensing for phase unwrapping of interferometric SAR data

We approach to the problem of wave-front reconstruction via phase unwrapping of interferograms observed by interferometric synthetic aperture radar (SAR), from the viewpoints of Bayesian statistical inference and compressed sensing. For this purpose, we apply sparse representation for compressed sensing to the Bayesian wave-front reconstruction model from SAR interferograms by Saika and Uezu [1]. In the formulation of the problem taking sparse representation into account, the MAP estimate is found to lead to a phase unwrapping algorithm which can be interpreted as a quadratic programming problem. Numerical experiments on an artificial wave-front make it clear that the algorithm effectively removes noise to reconstruct the wave-front, although it suffers from the errors similar to block noise in image processing.