I am interested in the potential of regular Sentinel-1 A/B SAR images, as they offer both opportunities and challenges for scientists monitoring Earth deformation through SAR image time series.
The primary goal of my PhD thesis is to develop a novel, robust, and sequential Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) approach that takes into account the structure
of the covariance matrix and temporal decorrelation models, thereby enhancing the efficiency of the Phase Linking method. This proposed methodology will deepen our understanding of Earth
deformation through SAR image time series and provide valuable insights for operational monitoring.
Selected publications
All publications are available via the Publications tab.
2025
TGRS
Sequential Covariance Fitting for InSAR Phase Linking
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.
The proposed approach demonstrates comparable performance to the offline COFI-PL method while achieving a reduction in computation time , of approximately 15% in both simulations and real data experiments.
Moreover, it outperforms state-of-the-art sequential approaches in terms of both accuracy and speed.
@article{el2025fitting,title={Sequential Covariance Fitting for InSAR Phase Linking},author={El Hajjar Dana; Ginolhac Guillaume; Yan Yajing; El Korso Mohammed Nabil},journal={{IEEE Transactions on Geoscience and Remote Sensing}},year={2025},doi={10.1109/TGRS.2025.3583566},keywords={Interferometric Phase Linking, sequential, newly acquired SAR images, covariance fitting problem},url={},volume={},number={},pages={},month={February},issn={}}
SPL
Robust Sequential Phase Estimation using Multi-temporal SAR Image Series
Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) exploits Synthetic Aperture Radar images time series (SAR-TS)
for surface deformation monitoring via phase difference (with respect to a reference image) estimation. Most of the actual
state-of-the-art MT-InSAR rely on temporal covariance matrix of the SAR-TS, assuming Gaussian distribution. However, these
approaches become computationally expensive when the time series lengthens and new images are added to the data vector.
This paper proposes a novel approach to sequentially integrate each newly acquired image using Phase Linking (PL) and
Maximum Likelihood Estimation (MLE). The methodology divides the data into blocks, using previous images and estimations
as a prior to sequentially estimate the phase of the new image. Actually, this framework allows to consider non Gaussian
distributions, such as a mixture of scaled Gaussian distribution, which is particularly important to consider when dealing
with urban areas.
@article{el2025robust,title={Robust Sequential Phase Estimation using Multi-temporal SAR Image Series},author={El Hajjar Dana; Ginolhac Guillaume; Yan Yajing; El Korso Mohammed Nabil},journal={{IEEE Signal Processing Letters}},year={2025},doi={10.1109/LSP.2025.3537334},keywords={Multi-Temporal Interferometric Synthetic Aperture Radar, Maximum Likelihood Estimation, covariance matrix estimation, mixture of Scaled Gaussian distribution},url={},volume={},number={},pages={},month={January},issn={}}
