Hybrid Machine Learning Model for Forest Height Estimation From TanDEM-X and Landsat Data
A hybrid machine learning model combining TanDEM-X InSAR and Landsat optical data for forest height estimation.
Publications
Peer-reviewed papers on SAR, InSAR, and forest/biomass retrieval.
Hybrid AI–Physical Modeling in Interferometric SAR: Bridging Data-Driven and Physics-Based Approaches for Enhanced Parameter Retrieval
Doctoral thesis on combining data-driven machine learning with physics-based scattering models to improve parameter retrieval from interferometric SAR.
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.
Characterization of Forest Structure Changes Exploiting TanDEM-X and GEDI Synergies
Discusses the synergetic combination of TanDEM-X interferometric measurements and GEDI lidar waveforms to map forest height and structure changes, comparing two TanDEM-X global coverages (2011-2013 and 2018-2020).
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...
Synergizing AI and Physical Models for TanDEM-X InSAR Forest Height Estimation: A Hybrid Approach over the Gabon
Presents the hybrid AI-physical forest height estimation approach applied over Gabon, at the TerraSAR-X/TanDEM-X Science Team Meeting.
Combining AI Techniques with Physical Models: Forest Height Inversion from TanDEM-X InSAR Data Using a Hybrid Modeling Approach
In the realm of artificial intelligence, specifically utilizing methodologies such as machine learning and deep learning, a conspicuous display of substantial potential across various parameter estimation problems has been demonstrated. However, such AI...
Combining TanDEM-X and GEDI Data For Mapping Forest Structure Parameter Dynamics
The synergy of TanDEM-X interferometric data with GEDI lidar full waveform measurements for large-scale forest mapping has been addressed in a number of studies in the last years. In a number of studies, the GEDI lidar full waveforms have been used to...
Machine Learning in Model-Based Forest Height Inversion
Model-based (PM) forest height inversion from Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) measurements is today an established application demonstrated and validated at large scales for a wide variety of boreal and tropical forest sites...
Towards a Symbiosis of Model-Based and Machine Learning Forest Height Estimation Based on TanDEM-X InSAR
There is a necessity for developing and incorporating retrieval models, including Physical Models (PMs) and Machine Learning (ML) models for the inversion of geophysical parameters from multi-parameter SAR data. Over the last two decades, interferometric...
Analysis of Multi-Frequency Polarimetric SAR Data over Permafrost Regions
Multi-frequency polarimetric SAR analysis of the 2018-2019 PermASAR airborne campaign over the permafrost region of Herschel Island, northwest Canada.
3D Matching of TerraSAR-X Derived Ground Control Points to Mobile Mapping Data
Machine learning algorithms for 3D matching of TerraSAR-X-derived ground control points with mobile LiDAR mapping data, for georeferencing of pole-like structures.