Object-based multipass InSAR via robust low-rank tensor decomposition

Published in IEEE Transactions on Geoscience and Remote Sensing, 2018

Recommended citation: Jian Kang, Yuanyuan Wang, Michael Schmitt, Xiao Xiang Zhu. "Object-based multipass InSAR via robust low-rank tensor decomposition". In IEEE Transactions on Geoscience and Remote Sensing, 2018.

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The most unique advantage of multipass synthetic aperture radar interferometry (InSAR) is the retrieval of longterm geophysical parameters, e.g., linear deformation rates, over large areas. Recently, an object-based multipass InSAR framework has been proposed by Kang, as an alternative to the typical single-pixel methods, e.g., persistent scatterer interferometry (PSI), or pixel-cluster-based methods, e.g., SqueeSAR. This enables the exploitation of inherent properties of InSAR phase stacks on an object level. As a follow-on, this paper investigates the inherent low rank property of such phase tensors and proposes a Robust Multipass InSAR technique via Objectbased low rank tensor decomposition. We demonstrate that the filtered InSAR phase stacks can improve the accuracy of geophysical parameters estimated via conventional multipass InSAR techniques, e.g., PSI, by a factor of 10–30 in typical settings. The proposed method is particularly effective against outliers, such as pixels with unmodeled phases. These merits, in turn, can effectively reduce the number of images required for a reliable estimation. The promising performance of the proposed method is demonstrated using high-resolution TerraSAR-X image stacks.


  title={Object-based multipass InSAR via robust low-rank tensor decomposition},
  author={Kang, Jian and Wang, Yuanyuan and Schmitt, Michael and Zhu, Xiao Xiang},
  journal={IEEE Transactions on Geoscience and Remote Sensing},