Better Coherence, Better Height: Fusing Physical Models and Deep Learning for Forest Height Estimation from Interferometric SAR Data
This work addresses the problem of accurate and generalizable forest height estimation for remote sensing applications, representing an incremental improvement by combining existing methods.
The paper tackles forest height estimation from SAR data by proposing CoHNet, an end-to-end framework that fuses deep learning with physics-informed constraints, resulting in improved accuracy and reliability of predictions.
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.