Recursive learning for incomplete SAR displacement time series for Earth deformation observation
22 Août 2022
Catégorie : Doctorant
Thesis title: Recursive learning for incomplete SAR displacement time series for Earth deformation observation
Institutions: LISTIC, Université Savoie Mont-Blanc, L2S, Université Paris Saclay
- M. Guillaume Ginolhac, firstname.lastname@example.org (04 50 09 65 83),
- Mme Yajing Yan, email@example.com (04 50 09 65 36),
- M. Nabil El Korso, firstname.lastname@example.org (01 40 97 41 39)
Funding:ANR REPED-SARIX project (already obtained)
Beginning date: October 2022
Ph.D. subject description:
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 [1-4]. 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.
1.Displacement times series estimation
Thanks to the aforementioned multi-temporal InSAR methods, the accuracy of the displacement velocity estimation has been revolutionized to millimeters per year. However, these methods are mainly retrospective analysis tools and do not allow efficient gradual integration of new SAR images that arrive over time, and it is necessary to restart part of or the whole displacement estimation processing chain, which would be prohibitively expensive in practice and does not answer the need for operational monitoring. In particular, the computational cost is significant when dealing with non-Gaussian data  in case of high-resolution SAR data. It is thus crucial to elaborate advanced recursive multi-temporal InSAR methods allowing for efficient gradual integration of new arriving SAR images. Some algorithms have already been developed in the literature, but they do not consider the non gaussianity of the data and their inherent structure [6,7].
In this Ph.D thesis, we aim to develop a novel robust and recursive multi-temporal InSAR approach for operational displacement estimation from SAR image time series. We consider the state-of-the-art Phase Linking approach as the baseline approach in which the sample covariance matrix of SAR image time series is fully exploited. First, we propose a sequential or recursive estimation of the covariance matrix of SAR images, considering the structure of the covariance matrix that is directly related to the decorrelation properties of the targets under observation. Second, we integrate temporal decorrelation models (with possible unknown parameters) providing prior information on the structure of the covariance matrix in the sequential or recursive estimation process to improve the efficiency. We then deploy the Expectation – Maximization (EM) algorithm to estimate jointly the unknown model parameters and the covariance matrix in an iterative way . The displacement time series can be later obtained from the properly estimated covariance matrix.
2.Missing data imputation in SAR displacement time series
Missing data can exist in the displacement time series, mainly due to the coherence loss that results in unreliable displacement estimations. Data gaps can hinder the full understanding of the phenomenon under observation. It is then necessary to fill the gaps by using some data imputation techniques .
The second objective of this Ph.D consists of imputing missing data in displacement time series, with the missing data mechanism taken into account by assuming statistical laws and estimating the parameters that describe these statistical laws. We propose to develop a new approach based on the recent reference of .
We consider the ‘’Piton de la Fournaise’’ and Merapi volcano test sites as proving ground for the developed approach in this Ph.D thesis. Both descending and ascending Sentinel-1 A/B acquisitions are available. GPS measurements from permanent GNSS stations are also available for results comparison and validation.
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