研究人员开发了一种名为基于集合的Transformer（Set-Based Transformer）的新型深度学习框架，以改进非接触式长波红外高光谱成像中的大气补偿。该轻量级模型接收来自不同距离的多个辐射测量值，以联合估计透过率、大气路径辐射和下行光谱。实验表明，该框架在MODTRAN生成的 数据集上实现了低光谱失真，并且相关的代码和数据集均已公开。 AI
影响 该模型有望在恶劣的大气条件下提高遥感和材料识别的准确性。
排序理由 该集群包含一篇学术论文，详细介绍了用于特定科学成像任务的新模型和方法。
在 Hugging Face Daily Papers 阅读 →
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
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 …
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 c…
A lightweight deep learning framework is presented for atmospheric compensation in passive long-wave infrared hyperspectral imaging, enabling joint estimation of transmittance, atmospheric path radiance, and downwelling spectrum from multi-range radiance measurements.