Sequential Covariance Fitting for InSAR Phase Linking

TL;DR

This paper introduces a sequential covariance fitting method for InSAR phase linking that efficiently incorporates new SAR images over time, reducing computational costs and improving scalability for large SAR datasets.

Contribution

It presents a novel sequential integration approach for COFI-PL, enabling efficient updating with new SAR images using Majorization-Minimization optimization.

Findings

Reduces computational complexity compared to traditional methods.

Enables efficient incorporation of new SAR images over time.

Improves scalability for large SAR image time series.

Abstract

Traditional Phase-Linking (PL) algorithms are known for their high cost, especially with the huge volume of Synthetic Aperture Radar (SAR) images generated by Sentinel-1 SAR missions. Recently, a COvariance Fitting Interferometric Phase Linking (COFI-PL) approach has been proposed, which can be seen as a generic framework for existing PL methods. Although this method is less computationally expensive than traditional PL approaches, COFI-PL exploits the entire covariance matrix, which poses a challenge with the increasing time series of SAR images. However, COFI-PL, like traditional PL approaches, cannot accommodate the efficient inclusion of newly acquired SAR images. This paper overcomes this drawback by introducing a sequential integration of a block of newly acquired SAR images. Specifically, we propose a method for effectively addressing optimization problems associated with phase-only complex vectors on the torus based on the Majorization-Minimization framework.