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AI Research
arXiv CS.AI·Jun 9·5 min read
Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging
TL;DR
Atmospheric compensation is crucial for accurate long-wave infrared (LWIR) hyperspectral imaging but has been largely neglected due to its complexity. A lightweight set-based deep learning framework was developed to process multiple radiance measurements for improved atmospheric compensation.
✦ Why It Matters
Engineers can implement this framework to enhance the accuracy of hyperspectral imaging systems in challenging environments.
Key Takeaways
- Utilize the set-based framework for improved atmospheric compensation.
- Access the publicly available dataset and code for experimentation.
- Analyze learned representations with a sparse autoencoder.
- Achieve low spectral distortion in LWIR imaging results.
How It Works
The framework processes multiple radiance measurements collected at different standoff ranges, allowing it to jointly estimate transmittance and atmospheric path radiance. By leveraging a set-based approach, it captures complex relationships in the data, enhancing the accuracy of atmospheric compensation.