Mitigating decorrelation effects on interferometric synthetic aperture radar (InSAR) time series data is challenging. The phase linking (PL) algorithm has been the key to handling signal decorrelations in the past 15 years. Numerous studies have been carried out to enhance its precision and computational efficiency. Different PL algorithms have been proposed, each with unique phase optimization approaches, such as the quasi-Newton method, equal-weighted and coherence-weighted factors, component extraction and selection SAR (CAESAR), and eigendecomposition-based algorithm (EMI). The differences among the PL algorithms can be attributed to the weight criteria adopted in each algorithm, which can be coherence-based, sparsity-based, or other forms of regularization. The PL algorithm has multiple applications, including SAR tomography (TomoSAR), enhancing distributed scatterers (DSs) to combine with persistent scatterers (PS) in PS and DS (PSDS) techniques, and compressed PSDS InSAR (ComSAR), where it facilitates the retrieval of the optimal phase from all possible measurements. This article aims to review PL techniques developed in the past 15 years. The review also underscores the importance of the PL technique in various SAR applications (TomoSAR, PSDS, and ComSAR). Finally, the deep learning (DL) approach is discussed as a valuable tool to improve the accuracy and efficiency of the PL process.

Interferometric Phase Linking: Algorithm, application, and perspective

Tebaldini S.
2023-01-01

Abstract

Mitigating decorrelation effects on interferometric synthetic aperture radar (InSAR) time series data is challenging. The phase linking (PL) algorithm has been the key to handling signal decorrelations in the past 15 years. Numerous studies have been carried out to enhance its precision and computational efficiency. Different PL algorithms have been proposed, each with unique phase optimization approaches, such as the quasi-Newton method, equal-weighted and coherence-weighted factors, component extraction and selection SAR (CAESAR), and eigendecomposition-based algorithm (EMI). The differences among the PL algorithms can be attributed to the weight criteria adopted in each algorithm, which can be coherence-based, sparsity-based, or other forms of regularization. The PL algorithm has multiple applications, including SAR tomography (TomoSAR), enhancing distributed scatterers (DSs) to combine with persistent scatterers (PS) in PS and DS (PSDS) techniques, and compressed PSDS InSAR (ComSAR), where it facilitates the retrieval of the optimal phase from all possible measurements. This article aims to review PL techniques developed in the past 15 years. The review also underscores the importance of the PL technique in various SAR applications (TomoSAR, PSDS, and ComSAR). Finally, the deep learning (DL) approach is discussed as a valuable tool to improve the accuracy and efficiency of the PL process.
2023
Electromagnetic interference
Time series analysis
Estimation
Tomography
Regulation
Decorrelation
Synthetic aperture radar
Interferometry
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1260483
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