Deformation Velocity-Based Regularization of Multibaseline SAR Interferometry

• Published in: IEEE geoscience and remote sensing letters Vol. 19; pp. 1 - 5
• Main Authors: Zamani, Roghayeh, Aghababaei, Hossein
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Coherence; Constraint modelling; Covariance matrices; Covariance matrix; Data acquisition; Deformation; Earth surface; Exploitation; Global positioning systems; GPS; Interferometric synthetic aperture radar; Interferometry; Maximum likelihood estimates; Maximum likelihood estimation; Multibaseline (MB) SAR interferometry; Optimization; Outliers (statistics); Parameter estimation; Parameters; Positioning systems; Regularization; regularization term; SAR (radar); Satellite constellations; Satellite observation; Satellites; Small satellites; Spatial data; Strain; Synthetic aperture radar; synthetic aperture radar (SAR); Synthetic aperture radar interferometry; Velocity; Mediterranean Area
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2020.3028244

Synthetic aperture radar (SAR) interferometry (InSAR) has shown great potential in the monitoring of Earth's surface and detection of the possible slow temporal deformations. Within the framework of multibaseline SAR interferometry, the availability of multiple interferograms obtained from multipass satellite observations can significantly improve the accuracy of the estimated target parameters, i.e., the residual height and the mean deformation velocity. The parameters can be estimated in the maximum likelihood (ML) sense and through the data covariance matrix. However, the presence of artifact and outliers may impair the parameter estimation, specifically when the candidate cells are subject to temporal decorrelation and atmospheric phase noise effects. In this letter, the exploitation of contextual spatial information is proposed to reduce the possible ambiguity and improve the accuracy of ML-based parameter estimation. The proposed approach adds a regularization term (or a constraint) to the ML's model in order to include the information about the scene velocity variation. Hence, the resulted nonconvex optimization is resolved using the graph-cut concept. The method is evaluated using the simulated and two real data sets acquired by Constellation of Small Satellites for Mediterranean basin Observation (COSMO-SkyMed) and Sentinel-1A sensors over Tehran, Iran; and the results are validated using the global positioning system-based measurements.