Doctoral student, Polytech Annecy-Chambéry
Email/ E-mail :
Dana.ElHajjar@univ-savoie.fr
Office / Bureau :
A117
Address / Adresse :
LISTIC, 5 chemin de bellevue, CS80439, 74 944 Annecy Cedex, France
Reaserch team/ Group :
AFuTé
Thesis Title/ Titre Thèse :
"Recursive learning for incomplete SAR displacement time series for Earth deformation observation".
Abstract/ Résumé :
The systematic acquisition of and free access to Sentinel-1 A/B Synthetic Aperture Radar (SAR) images covering Europe every 6 days (every 12 days elsewhere) provide scientists with both opportunities and challenges for operational monitoring of Earth deformation by SAR image time series. Actually, the displacement estimation with SAR image time series has been well studied with the development of numerous multi-temporal Interferometry SAR (InSAR) methods, such as Small BAseline Subset, Permanent Scatterer Interferometry, SqueeSAR, Phase Linking methods, Multi-link InSAR, CAESAR, Least-Square estimator and EMI. However, incremental methodological development still seems necessary to reply to the operational requirement, that is, the processing should be efficient and robust, and the results should be reliable.
_________________________________________________________________________________________________________________
Systematic acquisition and free access to Sentinel-1 A/B SAR (Synthetic Aperture Radar) images covering Europe every 6 days (every 12 days elsewhere) offer scientists both opportunities and challenges for operational monitoring of Earth deformation using SAR image time series. At present, the estimation of displacements using SAR image time series has been well studied thanks to the development of numerous multi-temporal SAR interferometry (InSAR) methods, such as small baseline subset, permanent scatterer interferometry, SqueeSAR, phase link methods, multi-link InSAR, CAESAR, least squares estimator and EMI. However, progressive methodological development still seems necessary to meet operational requirements, i.e. processing must be efficient and robust, and results must be reliable.
Keywords / Mots-clefs :
SAR interferometry, robust statistics, recursive estimation, missing data imputation, time series
_________________________________________________________________________________________________________________
SAR interferometry, robust statistics, robust estimation, imputation of missing data, time series
Publications :
Phd supervisor / Directeur de thèse :
Yajing YAN
Phd co-supervisors / Co-directors :
Mohammed Nabil EL KORSO
Guillaume GINOLHAC
Start of the thesis / Début de la thèse :
1/12/2022
Doctoral school / Ecole doctorale :
Sciences, Engineering, Environment (SIE)