Object-based Multipass InSAR via Robust Low Rank Tensor Decomposition

TL;DR

This paper introduces RoMIO, a robust low rank tensor decomposition method for object-based multipass InSAR, significantly improving geophysical parameter estimation accuracy and robustness against outliers using high-resolution TerraSAR-X data.

Contribution

It proposes a novel low rank tensor decomposition approach for object-based multipass InSAR, enhancing accuracy and outlier resistance over existing methods.

Findings

Improves geophysical parameter accuracy by a factor of 10-30.

Effectively handles outliers like unmodeled phase pixels.

Reduces the number of images needed for reliable estimation.

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.