在飞行中实时检测甲烷泄漏，缩短响应时间。
On-board ML for Trace Gas detection in Imaging Spectroscopy data
- 用轻量级边缘机器学习模型在机载端处理光谱数据
- 首次实现机载光谱数据中甲烷点源的实时检测
- 适合需要快速响应的环境监测与应急任务
航空与航天成像光谱观测数据可探测瞬时事件，如痕量气体排放。但现有处理流程依赖地面慢速处理，导致信息延迟，无法及时跟进。2026年3月东京野外试验中，我们利用搭载的AVIRIS-5传感器探索了机载光谱数据的边缘计算处理。受通信带宽限制，无法实时下传完整数据立方体，因此仅传输由高效小型机器学习模型预测的潜在事件。本研究首次实现了基于边缘机器学习的成像光谱数据中甲烷点源排放的机载检测。
原文摘要 · Abstract (English)
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.
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