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}}
2024
SLSIP
Sequential Phase Linking : Progressive Integration of SAR Images for Operational Phase Estimation
@poster{elhajjar_mdis2024,title={Sequential Phase Linking : progressive integration of SAR images foroperational phase estimation},author={El Hajjar Dana; Ginolhac Guillaume; Yan Yajing; El Korso Mohammed Nabil},journal={{MDIS}},year={2024},doi={https://doi.org/10.5281/zenodo.14726538},keywords={},url={},volume={},number={},pages={},month={November},issn={}}