Bayesian inference in optical measurement due to remote sensing to synthetic aperture radar interferometry

We investigated the Bayesian inferences using the maximize a posteriori (MAP) estimation for the problem of phase unwrapping in remote sensing using the synthetic aperture radar (SAR) interferometry. Then, in order to clarify performance of the Bayesian inference estimate, we carried out Monte Carlo simulation for a set of wave-fronts generated by an assumed true prior. Then, we clarified that optimal performance was achieved under the Bayes-optimal condition within statistical uncertainty. Then, we clarified that the present method was effective even for an artificial wave-front in remote sensing due to SAR interferometry. Also, we found that the Bayesian inference via the conjugate gradient method to derive the MAP solution for this problem. Using the numerical simulation for the wave-front, we found that the MAP estimation using the conjugate gradient method was effective for phase unwrapping as well as the MPM estimate approximately.