Dana El Hajjar

I joined the LISTIC laboratory (University Savoie Mont Blanc) and the L2S laboratory ( CentraleSupélec, University Paris-Saclay) in December 2022 as a PhD student under the supervision of Yajing YAN, Mohammed Nabil El Korso and Guillaume Ginolhac.

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

SPL

Robust Sequential Phase Estimation using Multi-temporal SAR Image Series

El Hajjar, Dana, Ginolhac, Guillaume, Yan, Yajing and 1 more author

IEEE Signal Processing Letters 2025

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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},
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                                                                              = 
                                                                              {Multi-Temporal Interferometric Synthetic Aperture Radar, Maximum Likelihood Estimation, covariance matrix estimation, mixture of Scaled Gaussian distribution}
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                                                                              {January}
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