Article Dans Une Revue IEEE Signal Processing Letters Année : 2025

Robust Sequential Phase Estimation using Multi-temporal SAR Image Series

Estimation séquentielle robuste de la phase à l'aide de séries d'images SAR multitemporelles

Résumé

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.
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Dates et versions

hal-04952062 , version 1 (17-02-2025)

Identifiants

Citer

Dana El Hajjar, Guillaume Ginolhac, Yajing Yan, Mohammed Nabil El Korso. Robust Sequential Phase Estimation using Multi-temporal SAR Image Series. IEEE Signal Processing Letters, inPress, ⟨10.1109/LSP.2025.3537334⟩. ⟨hal-04952062⟩
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