A near-real-time multi-temporal polarimetric InSAR method for landslides monitoring in rapid-decorrelation scenarios
Interferometric synthetic aperture radar (InSAR) technology can measure ground deformation with high precision over wide areas, which is essential for understanding natural hazards and ensuring infrastructure safety. However, in regions with dense vegetation or frequent surface changes, the radar ec...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:upcommonspor::93de0ba79cf7042c1c8fa407b4cbbefe |
| Acceso en línea: | https://hdl.handle.net/2117/461791 https://dx.doi.org/10.1016/j.isprsjprs.2026.02.006 |
| Access Level: | acceso embargado |
| Palabra clave: | Synthetic aperture radar interferometry (InSAR) Distributed scatterer (DS) Decorrelation Phase linking Polarimetric optimization Near-real-time monitoring Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Radar |
| Sumario: | Interferometric synthetic aperture radar (InSAR) technology can measure ground deformation with high precision over wide areas, which is essential for understanding natural hazards and ensuring infrastructure safety. However, in regions with dense vegetation or frequent surface changes, the radar echoes lose stability over time due to temporal decorrelation. This severely limits the reliability and accuracy of InSAR measurements. Many advanced processing methods have been developed to address this issue, and while they work well in stable conditions, their performance degrades sharply when coherence is lost rapidly. To overcome this limitation, this study proposes a near-real-time sequential multi-temporal polarimetric InSAR (MT-PolInSAR) method tailored for such conditions. For each new acquisition, a stack comprising only the latest images is formed, and statistically homogeneous pixels are reselected dynamically to adapt to evolving scattering mechanisms. A sequential polarimetric-temporal phase optimization is then applied within the stack that confines estimation to short, high-coherence windows and avoids coherence loss between stacks, thereby reducing the effect of fast temporal decorrelation. Deformation time series are subsequently updated through a sequential least squares (LS) inversion using only the newly formed interferograms, which eliminates the need to reprocess the whole dataset and enables timely updates. Experiments with simulated data and full-polarization ALOS-2 and dual-polarization Sentinel-1 images over Fengjie, China, demonstrate that the proposed method significantly increases coherent pixel density and improves deformation accuracy in rapid-decorrelation areas, while enabling genuine near-real-time monitoring with a more efficient processing strategy. |
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