A New Phase Linking Algorithm for Multi-temporal InSAR based on the Maximum Likelihood Estimator

Abstract

This paper presents a new algorithm for improving the estimation of interferometric SAR (InSAR) phases in the context of time series and phase linking approach. Based on maximum likelihood estimator of a multivariate Gaussian model, the estimation of the InSAR phases is solved using a Block Coordinate Descent algorithm. Compared to the state-of-the-art approaches, the main improvement lies on the joint estimation of the covariance matrix and the InSAR phases instead of using a plug-in coherence estimate obtained from the sample covariance of the data or the modeling of the temporal decorrelation of the target under observation. Results of synthetic simulations confirm the improvement brought by the proposed estimator.

Yajing Yan

Associate Professor of Remote Sensing for Earth Observation

My research interests include Interferometry SAR, multi-temporal analysis, data inversion, data assimilation and machine learning.

Guillaume Ginolhac

Professor of Statistical Signal Processing

My research interests include statistical learning, covariance estimation.