Robust Phase Linking in InSAR
Abstract
Phase linking is a prominent methodology to estimate coherence and phase difference in interferometric synthetic aperture radar. 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 phase linking 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 then yield new phase linking algorithms. In order to be robust to non-Gaussian data, we consider the use of a more general model of scaled mixture of Gaussian. To address small sample support issues, we also generalize this approach to a possibly low-rank structured covariance matrix. A unified algorithm to perform phase linking given these models is then derived and validated by simulations and a real data case (Sentinel-1 data).
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BibTeX
@article{vu2026robust,
title = {{Robust Phase Linking in InSAR}},
author = {Phan Viet Hoa Vu and Arnaud Breloy and Frédéric Brigui and Yajing Yan and Guillaume Ginolhac},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
year = {2026},
month = sep,
doi = {10.1109/tgrs.2023.3289338},
url = {https://hal.science/hal-04161539v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Robust Phase Linking in InSAR
AU - Phan Viet Hoa Vu
AU - Arnaud Breloy
AU - Frédéric Brigui
AU - Yajing Yan
AU - Guillaume Ginolhac
PY - 2026
DA - 2026/09/21
JO - IEEE Transactions on Geoscience and Remote Sensing
DO - 10.1109/tgrs.2023.3289338
UR - https://hal.science/hal-04161539v1
AB - Phase linking is a prominent methodology to estimate coherence and phase difference in interferometric synthetic aperture radar. 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 phase linking 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 then yield new phase linking algorithms. In order to be robust to non-Gaussian data, we consider the use of a more general model of scaled mixture of Gaussian. To address small sample support issues, we also generalize this approach to a possibly low-rank structured covariance matrix. A unified algorithm to perform phase linking given these models is then derived and validated by simulations and a real data case (Sentinel-1 data).
SN - 0196-2892
SN - 1558-0644
ER -