用于成像光谱数据中痕量气体检测的机载机器学习
On-board ML for Trace Gas detection in Imaging Spectroscopy data
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中文总结 AI 辅助
该研究针对成像光谱数据痕量气体检测的地面处理延迟问题，利用AVIRIS-5传感器数据，通过小型边缘机器学习模型实现了甲烷点源排放的首次机载检测。
中文摘要 AI 辅助
航空及星载成像光谱活动采集的数据可用于检测痕量气体排放等瞬态事件，但当前处理流程依赖缓慢的地面处理，延迟了每个检测事件的信息获取时间，无法立即采取后续行动。在2026年3月的东京野外活动中，我们探索了利用机载AVIRIS-5传感器的成像光谱数据进行机载处理。由于通信瓶颈，飞行期间无法立即下传完整数据立方体，因此我们下传高效小型机器学习模型预测的潜在事件。我们展示了首次利用边缘机器学习通过成像光谱数据进行甲烷点源排放的机载检测。
英文摘要
Data collected during aerial and spaceborne imaging spectroscopy campaigns enables the detection of transient events such as trace gas emissions. However, current processing pipelines depend on slow, on-the-ground processing, which delays the time to information of each detected event and prohibits immediate follow-up actions. During the Tokyo Field Campaign of March 2026, we explored on-board processing of Imaging Spectroscopy data from the equipped AVIRIS-5 sensor. Due to communication bottlenecks, full datacubes cannot be downlinked immediately during the flight. Instead we downlink the potential events predicted by our efficient and small machine learning model. We show the first on-board detection of methane point source emission with Imaging Spectroscopy data using Edge ML.
发表机构
- Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）
机构由 AI 辅助整理，请以论文原文为准。
