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={}}
Conference proceedings
2025
EUSIPCO
Sliding IPL : an efficient approach for estimating the phases of large SAR time series
Thanks to missions like Sentinel-1, with their shortrevisit time (e.g. 6-12 days), a vast amount of Synthetic Aper-ture Radar (SAR) images is now available.
This abundanceis advantageous for the accessibility of time series and theirsubsequent processing such as Multi-Temporal InterferometricSAR (MT-InSAR)
that allows for precise monitoring of landmotion. However, it poses challenges in managing such largedatasets efficiently while aiming to use them
in a resource-conscious and cost-effective manner. This contribution introducesa novel Interferometric Phase Linking (IPL) approach for SARimages eliminating the
need to store all past acquisitions whilestill leveraging their information. The method involves sliding atemporal window over the time series, allowing
the integration ofnew SAR acquisitions. This approach is faster than traditionalIPL methods, whether applied offline or sequentially. Its validityis
demonstrated using a Sentinel-1 SAR image time series tomonitor Mexico City.
@article{eusipco2025_elhajjar,title={Sliding IPL : an efficient approach for estimating the phases of large SAR time series},author={El Hajjar Dana; Breloy Arnaud; Ginolhac Guillaume; El Korso Mohammed Nabil; Yan Yajing},journal={{European Signal Processing Conference (EUSIPCO 2025)}},year={2025},doi={},keywords={phase estimation, covariance matrix estimation,sliding temporal window, SAR image, Interferometric PhaseLinking},url={},volume={},number={},pages={},month={September},issn={}}
GRETSI
Nouvelle approche pour l’estimation séquentielle et robuste de phase à partir de séries d’images SAR multi-temporelles
Cet article propose une approche séquentielle combinant le Phase Linking et l’estimation de maximum de vraisemblance.
Les données sont divisées en deux blocs : les acquisitions précédentes, utilisées comme a priori, et les nouvelles
images dont la phase est estimée. Contrairement aux méthodes classiques supposant une distribution gaussienne,
nous introduisons un modèle de mélange gaussien évolutif pour une meilleure robustesse aux données non gaussiennes.
L’approche est validée sur des images radars à synthèse d’ouverture de Sentinel-1.
@article{gretsi2025_elhajjar,title={Nouvelle approche pour l’estimation séquentielle et robuste de phase à partir de séries d’images SAR multi-temporelles},author={El Hajjar Dana; Ginolhac Guillaume; Yan Yajing; El Korso Mohammed Nabil},journal={{Groupe de Recherche et d'Etudes de Traitement du Signal et des Images (GRETSI)}},year={2025},doi={},url={},volume={},number={2025-1506},pages={p. 297-300},month={Août},issn={}}
GRETSI
Modèles de Flux Riemanniens pour l'Interférométrie SAR
Le bellier Georges, El Hajjar Dana, Breloy Arnaud and 1 more author
Groupe de Recherche et d'Etudes de Traitement du Signal et des Images (GRETSI) 2025
L'interférométrie SAR (InSAR) permet l'analyse des changements topologiques de sites naturels ou zones urbaines. Cependant, l'utilisation d'algorithmes de débruitage est nécessaire pour capturer l'information pertinente des interférogrammes. Dans cette article, nous exploitons la structure cyclique des phases interférométriques pour concevoir des modèles de transport de distributions efficaces et assurant la validité des données générées. Nous appliquons cette approche à i) la génération d'interférogrammes réalistes pour l'augmentation de données, ii) le débruitage d'interférogrammes. Nos expériences sur des données Sentinel-1 montrent l'efficacité des modèles de flux riemanniens pour ces applications.
@article{gretsi2025_lebellier,title={Modèles de Flux Riemanniens pour l'Interférométrie SAR},author={Le bellier Georges, El Hajjar Dana; Breloy Arnaud, Audebert Nicolas},journal={{Groupe de Recherche et d'Etudes de Traitement du Signal et des Images (GRETSI)}},year={2025},doi={},url={},volume={},number={2025-1653},pages={p. },month={Août},issn={}}
2024
IGARSS
Sequential Phase Linking : Progressive Integration of SAR Images for Operational Phase Estimation
This paper introduces a novel approach for sequential estimation of the interferometric phase in the context of long Synthetic Aperture Radar (SAR) image time series.
When newly acquired data arrive, the data set expands and can be partitioned into two distinct blocks. One represents the previous SAR images
and the other represents the newly acquired data. The proposed approach (S-MLE-PL) exploits sequential maximum likelihood estimation of the
covariance matrix of the whole data set, taking the existing data set as prior information. This approach facilitates the continuous
interferometric phase estimation by incorporating the new data into the previous context. In addition, it presents the advantage of reduced
computation time compared to the traditional approaches, making it a more efficient solution for operational displacement estimation.
@article{igarss_elhajjar_2024,title={Sequential Phase Linking : Progressive Integration of SAR Images for Operational Phase Estimation},author={El Hajjar Dana; Yan Yajing; Ginolhac Guillaume; El Korso Mohammed Nabil},journal={{International Geoscience and Remote Sensing Symposium (IGARSS 2024)}},year={2024},doi={10.1109/IGARSS53475.2024.10641742},keywords={Multi-temporal InSAR, sequential estimation, covariance matrix, phase linking},url={},volume={},number={},pages={11470-11473},month={July},issn={2153-6996}}
Thesis
2025
Thesis
Sequential Learning of SAR Image Time Series for Earth Deformation Monitoring
Synthetic Aperture Radar Interferometry (InSAR) is a remote sensing technique used to measure surface deformations with centimeter to millimeter level precision per year. This method relies on analyzing the phase differences between multiple Synthetic Aperture Radar (SAR) images acquired at different times.
Thanks to satellite missions like Sentinel-1, new radar images can now be acquired every 6 to 12 days, resulting in large temporal series of observations. While this dense temporal coverage provides significant potential for monitoring surface deformation, it also leads to a substantial increase in the volume of data to process.
To overcome these limitations, this thesis explores sequential phase estimation methods for MT-InSAR. Three sequential methods are proposed, including one based on Maximum Likelihood Estimation and two based on covariance fitting techniques. The approaches are validated on both synthetic and real datasets, demonstrating promising results.
@phdthesis{elhajjar2025thesis,
title = {Sequential Learning of SAR Image Time Series for Earth Deformation Monitoring},
author = {El Hajjar, Dana},
school = {University Savoie Mont Blanc},
year = {2025},
type = {PhD Thesis}
}