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Forest Height Retrieval from TanDEM-X InSAR

A hybrid AI-physical model combining data-driven learning with electromagnetic scattering models for forest height retrieval from TanDEM-X InSAR.

Core PhD research at DLR’s Microwaves and Radar Institute and ETH Zürich’s Chair of Earth Observation and Remote Sensing (2021-2025). Built and validated a hybrid AI-physical model that fuses neural networks with electromagnetic scattering models to retrieve forest height from TanDEM-X InSAR data, validated over tropical biomes in Gabon and the Amazon, and explicitly noted as relevant to ESA’s Biomass mission.

References

  1. Islam Mansour Hybrid AI–Physical Modeling in Interferometric SAR: Bridging Data-Driven and Physics-Based Approaches for Enhanced Parameter Retrieval .
  2. Islam Mansour, Kostas Papathanassiou, Ronny Hänsch, Irena Hajnsek Hybrid Machine Learning Forest Height Estimation From TanDEM-X InSAR . IEEE Transactions on Geoscience and Remote Sensing , 2025 .