Robust multibaseline InSAR optimization

Multibaseline SAR interferometry may face unmodeled interferometric phase such as unmodeled motion phase and uncompensated atmospheric phase, as well as non-Gaussian statistics in the context of distributed scatterer. We developed the robust InSAR optimization (RIO) [1] framework to systematically tackle these issues. Experiments show that RIO outperforms the current multibaseline InSAR methods in terms of the variance of the phase history parameters estimates for contaminated observations, while still keeping a relative efficiency of 80% for outlier-free observations.