An interferometric phase optimization method joining polarimetric and temporal dimensions
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...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/433323 |
| Acceso en línea: | https://hdl.handle.net/2117/433323 https://dx.doi.org/10.1109/TGRS.2025.3556141 |
| Access Level: | acceso abierto |
| Palabra clave: | Maximum likelihood estimation Polarization Synthetic aperture radar Interferometry Distributed scatterers (DSs) Maximum likelihood estimation (MLE) Multipolarization phase optimization Synthetic aperture radar interferometry (InSAR) Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Radar |
| Sumario: | 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. |
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