Multisensor Data Fusion for Enhanced Persistent Scatterer Analysis: Improving Ground Deformation Monitoring
The growing availability of multi-sensor Persistent Scatterer (PS) data offers high-precision, multi-temporal measurements for monitoring ground displacements across large areas. The ability to detect ground deformation phenomena is largely determined by PS density, influenced by the sensor resolution and site-specific characteristics. This research proposes a robust data fusion method to integrate multi-band/ multi-sensor PS products, prioritizing spatial dimensions to enhance coverage and exploit complementary information from diverse sensors. The method employs a weighted least squares approach with an adaptive weighting scheme to generate synthetic measurement points that synthesize deformations along the East-West and Up-Down components, effectively addressing challenges posed by sparse and unevenly distributed data. The methodology, validated through Sentinel-1 (S1) and COSMO-SkyMed (CSK) data integration, employs quantitative metrics and field observations to assess the reliability of fusion predictions. Statistical analysis demonstrates superior performance, with R-squared values of 0.950 for Up-Down and 0.868 for East-West components. Spatial coverage expands from approximately 10<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> (S1) and 22<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> (CSK) to 67<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of the total area of interest. The enhanced detection capabilities enable comprehensive monitoring of ground deformation processes, uncovering patterns otherwise undetectable through single-sensor analysis and providing crucial information for hazard assessment.
Authors
- Santiago Giraldo Manrique (ORCID: https://orcid.org/0009-0004-7861-9433)
- Gabriele Scarascia Mugnozza (ORCID: https://orcid.org/0000-0002-2917-0324)
- Francesca Bozzano (ORCID: https://orcid.org/0000-0002-0297-842X)
- Giorgia Berardo (ORCID: https://orcid.org/0000-0002-3279-7347)
- Carlo Alberto Stefanini (ORCID: https://orcid.org/0009-0003-1114-4616)
- Niccolò Belcecchi
- Michele Gaeta (ORCID: https://orcid.org/0000-0001-9445-8810)
- Paolo Mazzanti (ORCID: https://orcid.org/0000-0003-0042-3444)
- Claudia Masciulli (ORCID: https://orcid.org/0000-0001-6320-6071)
Institutions
- Ospedale Civile di Vittorio Veneto (IT)
- Sapienza University of Rome (IT)
Publication Details
- Journal
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/jstars.2025.3617779
- Primary Topic
- Landslides and related hazards
- Type
- article
- Field-Weighted Citation Impact
- 0.00