A Deep Learning Model to Predict GNSS from InSAR Data
- 2 January 2026
- book chapter
- Published by Springer Nature
- p. 339-351
Abstract
No abstract availableKeywords
This publication has 16 references indexed in Scilit:
- Novel algorithms for pair and pixel selection and atmospheric error correction in multitemporal InSARRemote Sensing of Environment, 2023
- Two-dimensional deformation monitoring for spatiotemporal evolution and failure mode of Lashagou landslide group, Northwest ChinaLandslides, 2022
- Fusion of GNSS and InSAR time series using the improved STRE model: applications to the San Francisco Bay Area and Southern CaliforniaJournal of Geodesy, 2022
- A robust method for 3-D surface displacement fields combining GNSS and single-orbit InSAR measurements with directional constraint from elasticity modelGPS Solutions, 2022
- Integrated Sentinel‐1 InSAR and GNSS Time‐Series Along the San Andreas Fault SystemJournal of Geophysical Research: Solid Earth, 2021
- Common-mode error and multipath mitigation for subdaily crustal deformation monitoring with high-rate GPS observationsGPS Solutions, 2021
- High‐Resolution Surface Velocities and Strain for Anatolia From Sentinel‐1 InSAR and GNSS DataGeophysical Research Letters, 2020
- The complete (3‐D) surface displacement field in the epicentral area of the 1999 MW7.1 Hector Mine Earthquake, California, from space geodetic observationsGeophysical Research Letters, 2001
- Synthetic aperture radar interferometryProceedings of the IEEE, 2000
- The Global Positioning System: Signals, measurements, and performanceInternational Journal of Wireless Information Networks, 1994