Better Coherence, Better Height: Fusing Physical Models and Deep Learning for Forest Height Estimation from Interferometric SAR Data
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. All code and models are available https://github.com/ragbm/CoHNet.
Cite
Text
Mahesh and Hänsch. "Better Coherence, Better Height: Fusing Physical Models and Deep Learning for Forest Height Estimation from Interferometric SAR Data." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2025.Markdown
[Mahesh and Hänsch. "Better Coherence, Better Height: Fusing Physical Models and Deep Learning for Forest Height Estimation from Interferometric SAR Data." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2025.](https://mlanthology.org/cvprw/2025/mahesh2025cvprw-better/)BibTeX
@inproceedings{mahesh2025cvprw-better,
title = {{Better Coherence, Better Height: Fusing Physical Models and Deep Learning for Forest Height Estimation from Interferometric SAR Data}},
author = {Mahesh, Ragini Bal and Hänsch, Ronny},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2025},
pages = {2329-2338},
url = {https://mlanthology.org/cvprw/2025/mahesh2025cvprw-better/}
}