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SAE-LWIR: A MODTRAN-Generated Dataset for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging

Dataset of the paper "Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging" (IGARSS 2026, Oral).

Fabian Perez, Nicolas Quintero, Jeferson Acevedo, Hoover Rueda-Chacón — Universidad Industrial de Santander, Bucaramanga, Colombia

Project Page · Code · Paper

Description

To the best of our knowledge, this is the first publicly available dataset tailored to atmospheric compensation (AC) in standoff long-wave infrared (LWIR) hyperspectral imaging. It was generated with MODTRAN5 from the clear-sky atmospheric profile database (CSP, derived from ECMWF ERA5), filtered to 36,547 clear-sky profiles (cloud coverage ≤ 10%, relative humidity ≤ 90%, subset of ocean-surface profiles removed).

At-sensor radiance follows the standoff radiative transfer equation, simulated at:

7 standoff ranges: R = {30, 90, 150, 210, 270, 330, 390} m
7 target temperatures: T = {280, 285, 290, 295, 300, 305, 310} K (gray-body emissivity ε = 0.95)
256 spectral bands over the 8–13 µm LWIR window (Gaussian ISRF, FWHM = 40 nm)

yielding 255,829 samples (36,547 profiles × 7 temperatures). Radiance is reported in microflicks (µW·sr⁻¹·cm⁻²·µm⁻¹). Downwelling radiance was computed at a fixed 45° viewing angle; transmittance and path radiance (upwelling) were computed along the line of sight for each range using gas concentrations specified independently for the first 126 atmospheric layers.

Files

File	Shape	Content
`forward.npy`	(36547, 7, 7, 256)	At-sensor radiance L — [profiles, temperatures, ranges, bands]
`transmittance.npy`	(36547, 7, 256)	Transmittance τ — [profiles, ranges, bands]
`upwelling.npy`	(36547, 7, 256)	Atmospheric path radiance Lₐ — [profiles, ranges, bands]
`downwelling.npy`	(36547, 256)	Shared downwelling spectrum L_d — [profiles, bands]
`processed_data/`	—	Raw atmospheric state variables per profile: temperature (`t.npy`), water vapor (`h2o.npy`), ozone (`o3.npy`), pressure (`p.npy`), skin temperature (`skt.npy`), surface pressure (`sp.npy`), total column water vapor (`tcwv.npy`), cloud coverage (`tcc.npy`), latitude/longitude, classification and clear-sky indices

Splits used in the paper: 70% train / 10% validation / 20% test (random).

Usage

hf download SemilleroCV/SAE-LWIR --repo-type dataset --local-dir data

import numpy as np

forward = np.load("data/forward.npy", mmap_mode="r")        # (36547, 7, 7, 256)
transmittance = np.load("data/transmittance.npy", mmap_mode="r")
upwelling = np.load("data/upwelling.npy", mmap_mode="r")
downwelling = np.load("data/downwelling.npy", mmap_mode="r")

Training/evaluation code: https://github.com/Factral/SAE-LWIR

Citation

If you find this dataset useful, please cite our paper (arXiv:2606.08324):

@inproceedings{perez2026setbased,
  title={Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging},
  author={Perez, Fabian and Quintero, Nicolas and Acevedo, Jeferson and Rueda-Chac{\'o}n, Hoover},
  booktitle={IGARSS 2026 - IEEE International Geoscience and Remote Sensing Symposium},
  year={2026}
}

Acknowledgments

This work was supported by the Air Force Office of Scientific Research (AFOSR) through the Southern Office of Aerospace Research and Development (SOARD) under grant number FA8655-25-1-8010.

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Paper for SemilleroCV/SAE-LWIR

Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging

Paper • 2606.08324 • Published Jun 6