  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.
