| title | Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging |
|---|---|
| authors | Fabian Perez, Nicolas Quintero, Jeferson Acevedo, Hoover Rueda-Chacon |
| date | 2026-06-06 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.08324 |
| links | abs pdf hf code |
| abstract | Passive long-wave infrared (LWIR) hyperspectral imaging under a standoff geometry depends on atmospheric absorption and emission, as well as reflected radiance, thus making atmospheric compensation essential to get knowledge of a target of interest. Despite its importance, this compensation has been largely overlooked due to its practical and modeling difficulty. In this paper, we present a lightweight set-based deep learning framework that takes multiple radiance measurements, collected at different standoff ranges, as input and jointly estimates transmittance, atmospheric path radiance, and a shared downwelling spectrum. We analyze the learned representation with a sparse autoencoder and observe that several latent features do activate on geographically coherent subsets of the test data despite the absence of location supervision. Experiments on a MODTRAN generated standoff LWIR dataset demonstrate low spectral distortion across all estimated products. The dataset and code is publicly available at: https://factral.co/SAE-LWIR/ |
cite
@misc{perez2026set,
title={Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging},
author={Fabian Perez and Nicolas Quintero and Jeferson Acevedo and Hoover Rueda-Chacon},
year={2026},
eprint={2606.08324},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.08324}
}