Monte Carlo simulation for phase unwrapping using super-resolution for remote sensing using synthetic aperture radar interferometry

We investigate the problem of phase unwrapping using multiple interferograms on the basis of Bayesian inference using the maximizer of the posterior marginal (MPM) estimate by making use of Monte Carlo simulation for several artificial wave-fronts in remote sensing via the synthetic aperture radar (SAR) interferometry. The simulations find that there is a phase where phase unwrapping is realized under the constraint of the surface-consistency in the hyper-parameter space, and that the upper phase boundary of the phase is steady along the parameter tuning fluctuations around the MAP solution. Also, we clarify that the MPM estimate succeeds in reconstruct the wave-front similar to the original one under the constraint of the surface-consistency condition, if the interferograms are corrupted by the Gaussian noises. Then, we find that the MPM estimate accurately reconstructs original wave-fronts using multiple interferograms, even if the interferograms are corrupted by Gaussian noises whose standard deviation is not so small.