Incorporating Physical Constraint in Three-Dimensional Time Series InSAR Inversion for Urban Deformation Monitoring
Spaceborne interferometric synthetic aperture radar (InSAR) has become a key method for remote sensing monitoring of surface deformations, providing millimeter-level accuracy along the satellite’s line-of-sight. However, precise understanding of deformation mechanisms requires three-dimen...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11015766/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Spaceborne interferometric synthetic aperture radar (InSAR) has become a key method for remote sensing monitoring of surface deformations, providing millimeter-level accuracy along the satellite’s line-of-sight. However, precise understanding of deformation mechanisms requires three-dimensional (3-D) measurements. Challenges in obtaining long time series and high-density in-situ measurements, along with the limited sensitivity of polar-orbiting SAR satellites to north-south displacements, often lead to suboptimal horizontal accuracy in conventional 3-D inversion. This study proposes a 3-D inversion method for time series InSAR measurements, specifically designed for scenarios with single-track SAR data, common in many areas where free data is limited. The method introduces a physical constraint between the horizontal deformation and the spatial gradients of vertical displacement, concentrated on urban areas where deformations are typically slow, long-term, and small in magnitude. Considering the localized and different drivers of urban deformations, such as diverse settlement patterns from tunneling activities and ocean reclamation, we propose a pixel-by-pixel estimation approach for the proportionality factor <inline-formula><tex-math notation="LaTeX">$\beta $</tex-math></inline-formula>, the key parameter in the physical constraint. This approach recognizes that <inline-formula><tex-math notation="LaTeX">$\beta $</tex-math></inline-formula> varies across locations, contrasting with the conventional assumption of a uniform <inline-formula><tex-math notation="LaTeX">$\beta $</tex-math></inline-formula> for the entire study area. An optimization process with gradient descent is introduced to iteratively refine <inline-formula><tex-math notation="LaTeX">$\beta $</tex-math></inline-formula>, accelerating convergence and achieving a robust solution. Experiments were conducted in representative scenarios, including ocean-reclaimed and metro-tunneling areas, with validation through leveling measurements. The results demonstrate that the proposed method achieves precise 3-D inversion and effectively captures the complex dynamics of urban surface deformation. |
|---|---|
| ISSN: | 1939-1404 2151-1535 |