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 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.
Highlights
We develop a novel tensor decomposition method in a complex domain, which jointly optimizes low rank and total variation (TV) terms for recovering outlier-free InSAR data stacks
The minimization of L(X, E, F, Z, T1, T2, T3) with respect to each variable can be solved by optimizing the following subproblems: 1) X Subproblem: By fixing the other variables, the subproblem of L with respect to X is μ min β. It can be solved by the singular value thresholding (SVT) operator [43], [44] on the mode-n(n = 1, 2, 3) unfolding of the tensor (1/2)(G − E + Z + (T1−T2/μ)), where SVT operator is defined as Sμ(A) := Udiag(max(σi − μ, 0))V with U, V and σi obtained from singular value decomposition (SVD) of the matrix A
This article proposed a novel tensor decomposition method in a complex domain based on the prior knowledge of the low rank property and smoothness structure in multipass InSAR
Summary
W ITH respect to different scattering cases, i.e., point scatterers and distributed scatterers, methods for the retrieval of geophysical parameters (namely elevation and deformation parameters) for large areas can be split into two categories: persistent scatterer interferometry (PSI) [2]–[11] and distributed scatterer interferometry (DSI) [12]–[18]. [1] investigated the inherent low rank property of multipass InSAR phase tensors It allows loose semantic labels, such as a rectangle covering the major part of an object, for object-based geophysical parameter reconstruction in urban areas. As a follow-on work, we seek to develop a novel method for parameter retrieval from multipass InSAR data stacks by jointly considering the variational prior [36] and the low rank property [1] of InSAR stacks To this end, a TV regularized robust low rank tensor decomposition method in a complex domain is proposed in this article in order to recover outlierfree InSAR data stacks
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