The Robust InSAR Optimization Framework with Application to Monitoring Cities on Volcanoes

This paper introduces the Robust InSAR Optimization (RIO) framework to the multi-pass InSAR techniques, such as PSI, SqueeSAR and TomoSAR whose current optimal estimators were derived based on the assumption of Gaussian distributed stationary data, with seldom attention towards their robustness. The {RIO} framework effectively tackles two common problems in the multi-pass InSAR techniques: 1. treatment of images with bad quality, especially those with large uncompensated atmospheric phase, and 2. the covariance matrix estimation of non-stationary distributed scatterer ({DS}). The former problem is dealt with using a robust M-estimator which effectively down-weight the images that heavily violate the model, and the latter is addresses with a new method: the Rank M-Estimator ({RME}) by which the covariance is estimated using the rank of the {DS}. {RME} requires no flattening/estimation of the interferometric phase, thanks to the property of mean invariance of rank. The robustness of {RME} is achieved by using an M-estimator, i.e. amplitude-based weighing function in covariance estimation. The {RIO} framework can be easily extended to most of the multi-pass {InSAR} techniques.     «

This paper introduces the Robust InSAR Optimization (RIO) framework to the multi-pass InSAR techniques, such as PSI, SqueeSAR and TomoSAR whose current optimal estimators were derived based on the assumption of Gaussian distributed stationary data, with seldom attention towards their robustness. The {RIO} framework effectively tackles two common problems in the multi-pass InSAR techniques: 1. treatment of images with bad quality, especially those with large uncompensated atmospheric phase, and 2...     »