Better Coherence, Better Height: Fusing Physical Models and Deep Learning for Forest Height Estimation from Interferometric SAR Data

2025/04/14 by Ragini Bal Mahesh, Ronny Hänsch, Mahesh, Ragini Bal +1
Earth and Planetary Sciences · Engineering · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #Cryospheric studies and observations #FOS: Computer and information sciences #Landslides and related hazards #Synthetic Aperture Radar (SAR) Applications and Techniques

paper · pdf · doi:10.48550/arxiv.2504.10395

openalex publication_date 2025/04/14 · openalex created_date 2025/10/14 · openalex updated_date 2026/07/28

Abstract

Estimating forest height from Synthetic Aperture Radar (SAR) images often relies on traditional physical models, which, while interpretable and data-efficient, can struggle with generalization. In contrast, Deep Learning (DL) approaches lack physical insight. To address this, we propose CoHNet - an end-to-end framework that combines the best of both worlds: DL optimized with physics-informed constraints. We leverage a pre-trained neural surrogate model to enforce physical plausibility through a unique training loss. Our experiments show that this approach not only improves forest height estimation accuracy but also produces meaningful features that enhance the reliability of predictions.

Citations

• CATSNet: a context-aware network for Height Estimation in a Forested Area based on Pol-TomoSAR data
• Deep Learning Model Transfer in Forest Mapping using Multi-source Satellite SAR and Optical Images
• Physics-informed machine learning
• Living in the Physics and Machine Learning Interplay for Earth Observation
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Synthetic aperture radar interferometry

Related

• An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data
• Tomographic SAR Reconstruction for Forest Height Estimation
• 3D-SAR Tomography and Machine Learning for High-Resolution Tree Height Estimation
• High-Resolution Forest Mapping from L-Band Interferometric SAR Time Series using Deep Learning over Northern Spain
• CATSNet: a context-aware network for Height Estimation in a Forested Area based on Pol-TomoSAR data