Multipass SAR Interferometry Based on Total Variation Regularized Robust Low Rank Tensor Decomposition

Abstract

Multipass SAR interferometry (InSAR) techniques based on meter-resolution spaceborne SAR satellites, such as TerraSAR-X or COSMO-SkyMed, provide 3D reconstruction and the measurement of ground displacement over large urban areas. Conventional methods such as persistent scatterer interferometry (PSI) usually requires a fairly large SAR image stack (usually in the order of tens) to achieve reliable estimates of these parameters. Recently, low rank property in multipass InSAR data stack was explored and investigated in our previous work (J. Kang et al., 'Object-based multipass InSAR via robust low-rank tensor decomposition,' IEEE Trans. Geosci. Remote Sens., vol. 56, no. 6, 2018). By exploiting this low rank prior, a more accurate estimation of the geophysical parameters can be achieved, which in turn can effectively reduce the number of interferograms required for a reliable estimation. Based on that, this article proposes a novel tensor decomposition method in a complex domain, which jointly exploits low rank and variational prior of the interferometric phase in InSAR data stacks. Specifically, a total variation (TV) regularized robust low rank tensor decomposition method is exploited for recovering outlier-free InSAR stacks. We demonstrate that the filtered InSAR data stacks can greatly improve the accuracy of geophysical parameters estimated from real data. Moreover, this article demonstrates for the first time in the community that tensor-decomposition-based methods can be beneficial for large-scale urban mapping problems using multipass InSAR. Two TerraSAR-X data stacks with large spatial areas demonstrate the promising performance of the proposed method.