Low rank modeling-based multipass InSAR technique

Abstract

During the last few decades, multipass InSAR techniques have been developed for the retrieval of long term geophysical parameters, e.g. linear deformation rates, over large areas. Conventional method such as Persistent Scatter Interferometry (PSI) usually requires a fairly large SAR image stack (usually in the order of tens), in order to achieve reliable estimates of the parameters. However, in case of limited number of SAR images, e.g. less than 10, not only will the efficiency of the estimator decrease to an unacceptable level, the estimates will also suffer from large bias because of the asymptotic optimality of many typical estimators used in multipass InSAR, such as the periodogram used in PSI. In this paper, a novel multipass InSAR technique based on low rank modeling of complex-valued InSAR phase stacks is introduced. By combining this technique with PSI, we demonstrate that the proposed framework can improve the accuracy of geophysical parameters estimated via PSI by a factor of ten to thirty in typical settings, especially with a limited number of SAR images for the reconstruction.