Doctoral thesis on combining data-driven machine learning with physics-based scattering models to improve parameter retrieval from interferometric SAR.
#insar
Content tagged with "insar"
Hybrid AI–Physical Modeling in Interferometric SAR: Bridging Data-Driven and Physics-Based Approaches for Enhanced Parameter Retrieval
Hybrid AI-physical Modeling for Penetration Bias Correction in X-band InSAR DEMs: A Greenland Case Study
A hybrid AI-physical method to correct penetration bias in X-band InSAR DEMs over the Greenland Ice Sheet, parameterising the vertical structure function via machine learning.
Hybrid Machine Learning Forest Height Estimation From TanDEM-X InSAR
A hybrid model-based and machine learning approach to forest height estimation from TanDEM-X InSAR, validated over tropical biomes including Gabon and the Amazon.
Correction of The Penetration Bias for InSAR DEM Via Synergetic AI-Physical Modeling: A Greenland Case Study
Rapid changes in the Greenland Ice Sheet require precise elevation monitoring to understand ice dynamics and predict sea level rise. X-band Interferometric Synthetic Aperture Radar (InSAR) has the potential for this purpose but is limited by microwave signal...