Robust multibaseline InSAR optimization

Multibaseline / multipass 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 greatly outperform the current multipass InSAR methods in terms of the variance of the phase history parameter estimates for contaminated observations, while still keeping an relative efficiency of 80% for outlier-free observations