  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 method such as Persistent Scatterer Interferometry (PSI)
usually requires a fairly large SAR image stack (usually in the order of tens),
in order to achieve reliable estimates of these parameters. Recently, low rank
property in multipass InSAR data stack was explored and investigated in our
previous work. By exploiting this low rank prior, 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 paper proposes a novel tensor decomposition method in 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 paper 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.

Signal Processing