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Hybrid AI–Physical Modeling in Interferometric SAR: Bridging Data-Driven and Physics-Based Approaches for Enhanced Parameter Retrieval

Islam Mansour · 2025 · Chair of Earth Observation and Remote Sensing. Supervised by Prof. Dr. Irena Hajnsek, co-advised by Dr. Kostas Papathanassiou (PolInSAR / tomographic SAR), Dr. Ronny Hänsch (Machine Learning), and Dr. Georg Fischer (Cryosphere application).

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Abstract

Doctoral thesis on combining data-driven machine learning with physics-based scattering models to improve parameter retrieval from interferometric SAR. Covers forest height retrieval from TanDEM-X and penetration bias correction in X-band InSAR DEMs.

Citation

@phdthesis{mansour2025thesis,
  title = {Hybrid AI--Physical Modeling in Interferometric SAR: Bridging Data-Driven and Physics-Based Approaches for Enhanced Parameter Retrieval},
  author = {Mansour, Islam},
  school = {ETH Zurich},
  year = {2025},
  doi = {10.3929/ETHZ-C-000783509},
  pages = {186}
}