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.

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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
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article

Multisensor Data Fusion for Enhanced Persistent Scatterer Analysis: Improving Ground Deformation Monitoring

Santiago Giraldo Manrique, Gabriele Scarascia Mugnozza, Francesca Bozzano, Giorgia Berardo et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Landslides and related hazards
article

Multisensor Data Fusion for Enhanced Persistent Scatterer Analysis: Improving Ground Deformation Monitoring

Santiago Giraldo Manrique, Gabriele Scarascia Mugnozza, Francesca Bozzano, Giorgia Berardo, Carlo Alberto Stefanini, Niccolò Belcecchi, Michele Gaeta, Paolo Mazzanti, Claudia Masciulli
article en

Abstract

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.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingVol. 18
Ospedale Civile di Vittorio Veneto (IT), Sapienza University of Rome (IT)
Openalex Percentile: Top 63%
Landslides and related hazards
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