A New Likelihood Function for Consistent Phase Series Estimation in Distributed Scatterer Interferometry

The proper use of distributed scatterer (DS) can improve both the density and quality of synthetic aperture radar (SAR) interferometry (InSAR) measurements. A critical step in DS interferometry (DSI) is the restoration of a consistent phase series from SAR interferogram stacks. Most state-of-the-art...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-14
Main Authors: Wang, Chisheng, Wang, Xiang-Sheng, Xu, Yaping, Zhang, Bochen, Jiang, Mi, Xiong, Siting, Zhang, Qin, Li, Weidong, Li, Qingquan
Format: Article
Language:English
Subjects:
Airports
Algorithms
Approximation
Coherence
Cost function
Density
Distributed scatterer (DS)
Frameworks
Interferometric synthetic aperture radar
Interferometry
Mathematical analysis
maximum likelihood
Maximum likelihood estimation
phase-linking (PL)
Restoration
SAR (radar)
Scattering
Strain
Synthetic aperture radar
synthetic aperture radar (SAR)
Time series analysis
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Summary:The proper use of distributed scatterer (DS) can improve both the density and quality of synthetic aperture radar (SAR) interferometry (InSAR) measurements. A critical step in DS interferometry (DSI) is the restoration of a consistent phase series from SAR interferogram stacks. Most state-of-the-art algorithms adopt an approximate likelihood function to calculate the likelihood by replacing the true coherence matrix with its estimation, more specifically, the sample coherence matrix (SCM). However, this approximation has a drawback in that the coherence estimates are greatly biased when the coherence is low. In this study, we derive a new likelihood function without such an approximation. Accordingly, a DSI framework using this function for phase estimation and point selection is provided. In this framework, the new likelihood function serves as a cost function for phase estimation and a quality measure for DS selection. Its performance is investigated by experiments in a simulation study and a real-world case study using Sentinel-1 data over Shenzhen airport in China. The results reveal that the proposed DSI framework outperforms the existing state-of-the-art approaches in different scenarios, in terms of providing a more accurate estimation and improving DS density and coverage.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2022.3170567