# A Deep Learning Approach for SAR Tomographic Imaging of Forested Areas

arXiv: https://arxiv.org/abs/2301.08605 · HTML: https://mlsanity.com/paper/2301.08605/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 20 Jan 2023.

## Builds on (most cited first, 2 of 2)

- [boldsymbolγ-Net: Superresolving SAR Tomographic Inversion via Deep Learning](https://mlsanity.com/paper/2112.04211/) (2021)
- [AETomo-Net: A Novel Deep Learning Network for Tomographic SAR Imaging Based on Multi-dimensional Features](https://mlsanity.com/paper/2209.11038/) (2022)

## Built on by (most cited first, 3 of 3)

- [OpenForest: A data catalogue for machine learning in forest monitoring](https://mlsanity.com/paper/2311.00277/) (2023)
- [CATSNet: a context-aware network for Height Estimation in a Forested Area based on Pol-TomoSAR data](https://mlsanity.com/paper/2403.20273/) (2024)
- [An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data](https://mlsanity.com/paper/2507.20798/) (2025)

## Abstract (arXiv)

Synthetic aperture radar tomographic imaging reconstructs the three-dimensional reflectivity of a scene from a set of coherent acquisitions performed in an interferometric configuration. In forest areas, a large number of elements backscatter the radar signal within each resolution cell. To reconstruct the vertical reflectivity profile, state-of-the-art techniques perform a regularized inversion implemented in the form of iterative minimization algorithms. We show that light-weight neural networks can be trained to perform the tomographic inversion with a single feed-forward pass, leading to fast reconstructions that could better scale to the amount of data provided by the future BIOMASS mission. We train our encoder-decoder network using simulated data and validate our technique on real L-band and P-band data.