Practical Demonstration of Robust InSAR Optimization for Multipass InSAR

Multipass / multibaseline SAR interferometric measurements may suffer from, for example, non-Gaussian scatterers in the context of distributed scatterers, and unmodeled interferometric phase such as unmodeled motion phase and atmospheric phase. Robust InSAR Optimization (RIO) is a framework that systematically tackles these problems. Experiments show that RIO greatly outperform the current methods in terms of the variance of phase history parameter estimates for contaminated observations, but still keeps an relative efficiency of 80% when the observations are outlier-free.