Abstract
Phase linking (PL) is a prominent methodology to estimate coherence and phase difference in interferometric synthetic-aperture radar (InSAR). This method is driven by a maximum likelihood estimation approach, which allows to fully exploit all the possible interferograms from a time series. Its performance is, however, known to be affected by the accuracy of the covariance matrix estimation step, which usually requires to introduce additional prior information on its structure when there is a small sample support (spatial window). Moreover, most PL algorithms are built upon the sample covariance matrix, due to the assumption of an underlying Gaussian distribution. In a scenario where SAR data is high resolution or when the study area is spatially heterogeneous (e.g., urban area), this assumption can also limit the accuracy of the covariance matrix estimation step. Considering the two aforementioned issues, we introduce alternative statistical models, whose maximum likelihood estimators (MLEs) then yield new PL algorithms. In order to be robust to non-Gaussian data, we consider the use of a more general model of a scaled mixture of Gaussian. To address small sample support issues, we also generalize this approach to a possibly low-rank (LR) structured covariance matrix. A unified algorithm to perform PL given these models is then derived and validated by simulations and a real data case (Sentinel-1 data).
