Abstract
On the basis of analogy between Bayesian inference and statistical mechanics, we construct a method of wave-front reconstruction in remote sensing using the synthetic aperture radar (SAR) interferometry. Here, we use the maximizer of the posterior marginal (MPM) estimate for phase unwrapping and maximum entropy for noise reduction from unwrapped wave-fronts. Next, we investigate static property of the MPM estimate from a phase diagram described by using Monte Carlo simulation for a wave-front which is typical in remote sensing using the SAR interferometry. The phase diagram clarifies that phase unwrapping is accurately realized by the MPM estimate using an appropriate model prior under the constraint of surface-consistency condition, and that the MPM estimate smoothly carries out phase unwrapping utilizing fluctuations around the MAP solution. Also, using the Monte Carlo simulations, we clarify that the method of maximum entropy using an appropriate model prior succeeds in reducing noises from the unwrapped wave-front obtained by the MPM estimate.
