全自动痕量气体羽流检测
Fully Automatic Trace Gas Plume Detection
AI总结：
针对未来成像光谱仪数据量剧增，提出结合机器学习形态分析与光谱模型拟合的全自动痕量气体羽流检测方法，可自动检测标记羽流，用于回顾性分析，还扩展到三种痕量气体检测。
摘要：
未来成像光谱仪将使当代数据量大幅增加，需要自动化方法来提升痕量气体点源的劳动密集型检测。本文提出一种全自动方法，无需人工参与即可实现羽流检测和标记的操作性能。该方法将基于机器学习的形态分析与基于物理的光谱模型拟合相结合。在EMIT成像光谱仪数据上进行了两种模式的部署。首先是“每日摘要”模式，能自动处理所有下行数据，标记最大事件以便立即响应，此模式检测准确率高且误报率可忽略不计。其次用于回顾性分析，发现现有人工审核流程遗漏的羽流，观察到至少25%的大羽流可能因确认偏差和视觉线索模糊而被忽略。最后将检测扩展到三种研究较少的痕量气体：NH3、NO2以及EMIT图像中首次观测到的一氧化碳（CO）羽流。
英文摘要：
Future imaging spectrometers will expand contemporary data volumes by orders of magnitude, requiring automated methods to upscale labor-intensive detection of trace gas point sources. Here we present a fully-automated approach that achieves operational performance for plume detection and labelling without human participation. Our method combines machine learning (ML)-based morphological analysis with physics-based spectroscopic model fitting. We deploy it on data from the EMIT imaging spectrometer, operating in two modes. First, we present a "daily digest" that runs automatically on all downlinked data, flagging the largest events for immediate response. The daily digest demonstrates that a significant fraction of the largest plumes can be detected automatically with negligible false positives. This represents a significant new high-water mark in plume detection accuracy. Second, we use it for retrospective analysis to find plumes that were missed by the existing human review process. We observe that at least 25% of large plumes may have been passed over in the existing workflow due to confirmation bias and ambiguity in the visual cues used by human reviewers. Finally, we extend detection to three understudied trace gases: NH3, NO2 and the first observations of carbon monoxide (CO) plume in EMIT imagery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
