arXiv:2606.08324v1 Announce Type: cross 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 estima
Advances in set-based deep learning and sensor technologies are enabling more sophisticated atmospheric compensation techniques essential for accurate remote sensing.
Accurate atmospheric compensation is critical for applications like defense, environmental monitoring, and target identification in challenging standoff scenarios.
This research introduces a more practical and effective method for atmospheric compensation in LWIR hyperspectral imaging, potentially enhancing intelligence gathering and environmental insights.
- · Defense contractors
- · Environmental monitoring agencies
- · Hyperspectral imaging companies
- · AI/ML researchers
- · Legacy atmospheric compensation methods
Improved accuracy and reliability of standoff LWIR hyperspectral imaging data for various applications.
Enhanced capabilities for target detection, classification, and environmental assessment in difficult conditions.
Potential for new autonomous systems to integrate enhanced sensing for better real-time decision-making in defense and climate monitoring.
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Read at arXiv cs.AI