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arXiv · 1801.07499

Object-based Multipass InSAR via Robust Low Rank Tensor Decomposition

Jian Kang·Yuanyuan Wang·Michael Schmitt·Xiao Xiang Zhu
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Abstract

The most unique advantage of multipass SAR interferometry (InSAR) is the retrieval of long term geophysical parameters, e.g. linear deformation rates, over large areas. Recently, an object-based multipass InSAR framework has been proposed in [1], 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 followon, this paper investigates the inherent low rank property of such phase tensors, and proposes a Robust Multipass InSAR technique via Object-based low rank tensor decomposition (RoMIO). 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 ten to thirty 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.

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Jian Kang, Yuanyuan Wang, Michael Schmitt, Xiao Xiang Zhu. 2018-01-23. Object-based Multipass InSAR via Robust Low Rank Tensor Decomposition. https://doi.org/10.1109/tgrs.2018.2790480

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