Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging
Researchers have developed a new deep learning framework called a Set-Based Transformer to improve atmospheric compensation in standoff long-wave infrared hyperspectral imaging. This lightweight model takes multiple radiance measurements from varying distances to jointly estimate transmittance, atmospheric path radiance, and downwelling spectrum. Experiments show that the framework achieves low spectral distortion on a MODTRAN-generated dataset, and the associated code and dataset are publicly available. AI
IMPACT This model could improve the accuracy of remote sensing and material identification in challenging atmospheric conditions.