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An Interferometric Phase Optimization Method Joining Polarimetric and Temporal Dimensions
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DOI：10.1109/TGRS.2025.3556141.png)
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摘要
En 中文
The polarimetric phase optimization method has been integrated into the multitemporal synthetic aperture radar interferometry (MT-InSAR) framework to enhance phase quality and deformation coverage, known as multitemporal polarimetric InSAR (MT-PolInSAR) technology. However, most existing MT-PolInSAR methods optimize phase separately in the temporal and polarimetric dimensions, failing to leverage the interdimensional relationships fully. This article proposes a novel multipolarization optimization method, which achieves one-step phase optimization by joining temporal and polarimetric dimensions based on a joint probability density function and maximum likelihood estimation (MLE). Additionally, a no-threshold regularization is employed to strengthen the stability of the multipolarization covariance matrix. The proposed approach has been validated through synthetic and real quad-polarization datasets. Regarding the real data, ALOS-2/PARSAR-2 from the Fengjie landslide in China and Radarsat-2 data from the Barcelona airport in Spain are used. The experimental outcomes demonstrate that our proposed approach significantly diminishes phase noise while increasing the density of measurement points.
Keyword:
distributed scatterers (DSs)
maximum likelihood estimation (MLE)
multipolarization phase optimization
synthetic aperture radar interferometry (InSAR)
distributed scatterers (DSs)
maximum likelihood estimation (MLE)
multipolarization phase optimization
synthetic aperture radar interferometry (InSAR)
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
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