Correction of The Penetration Bias for InSAR DEM Via Synergetic AI-Physical Modeling: A Greenland Case Study

Abstract

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 penetration biases, which can be a few meters. We present a novel hybrid modeling approach that integrates machine learning (ML) with physical models to enhance the estimation of the elevation bias in InSAR data at X-band.

Citation

@inproceedings{mansour2024correction,
  title = {Correction of The Penetration Bias for InSAR DEM Via Synergetic AI-Physical Modeling: A Greenland Case Study},
  author = {Mansour, Islam and Fischer, Georg and Hänsch, Ronny and Hajnsek, Irena and Papathanassiou, Kostas},
  booktitle = {IGARSS 2024 -- 2024 IEEE International Geoscience and Remote Sensing Symposium},
  pages = {138--142},
  year = {2024},
  doi = {10.1109/IGARSS53475.2024.10642748}
}