A Markovian Approach for InSAR Phase Reconstruction With Mixed Discrete and Continuous Optimization

In this letter, we propose a Markovian approach for interferometric synthetic aperture radar (InSAR) phase reconstruction. Recently, Markovian models based on multichannel InSAR likelihood statistics and total variation prior have been proposed to reconstruct the noisy and wrapped phase. Efficient discrete optimization algorithms based on the graph-cut technique are used to efficiently minimize the energy. Our contribution consists in extending these works to cope with continuous label sets providing more precise and accurate reconstructed profiles. The proposed approach also provides a good way to estimate local hyperparameters to adjust the prior model and preserve well discontinuities in profiles. This task is useful when working with real InSAR data where the quantization of the continuous label set leads to a loss of some physical information. The proposed method is compared to other Markovian approaches with discrete multilabel optimization algorithms. Experiments show better quality results both on simulated and real InSAR data.