Robust Sequential Phase Estimation Using Multi-Temporal SAR Image Series

Dana El Hajjar

doi:10.1109/LSP.2025.3537334

Robust Sequential Phase Estimation Using Multi-Temporal SAR Image Series

2025-01-01

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Mohammed Nabil El Korso

DOI：10.1109/LSP.2025.3537334

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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.

Keywords:

Covariance matrices

Coherence

Synthetic aperture radar

Signal processing algorithms

Radar polarimetry

Maximum likelihood estimation

Vectors

Time series analysis

Radar imaging

Gaussian distribution

Multi-temporal interferometric synthetic aperture radar

maximum likelihood estimation

covariance matrix estimation

mixture of scaled Gaussian distribution

Robust Sequential Phase Estimation Using Multi-Temporal SAR Image Series

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