A Hybrid Spatio-Temporal Graph Attention (ST D-GAT Framework) for Imputing Missing SBAS-InSAR Deformation Values to Strengthen Landslide Monitoring

A Hybrid Spatio-Temporal Graph Attention (ST D-GAT Framework) for Imputing Missing SBAS-InSAR Deformation Values to Strengthen Landslide Monitoring

Reservoir-induced landslides threaten infrastructures and downstream communities, making continuous deformation monitoring vital. Time-series InSAR, notably the SBAS algorithm, provides high-precision surface-displacement mapping but suffers from voids due to layover/shadow effects and temporal deco...

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Bibliographic Details
Main Authors: Hilal Ahmad, Yinghua Zhang, Hafeezur Rehman, Mehtab Alam, Zia Ullah, Muhammad Asfandyar Shahid, Majid Khan, Aboubakar Siddique
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Remote Sensing
Subjects:
InSAR
deep learning
hybrid graph attention network
missing data imputation
spatio-temporal modelling
landslide monitoring
Online Access:https://www.mdpi.com/2072-4292/17/15/2613
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