利用机器学习集成广义时空稳健框架检测NASA Black Marble夜间异常
Detecting Nighttime Anomalies from NASA Black Marble Using a Generalized Spatio-Temporally Robust Framework of Machine Leaning Ensembles
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中文总结 AI 辅助
提出联合建模Black Marble M波段与DNB信号的机器学习集成框架，构建时空稳健的异常检测器，提升真实检测率并减少误报，适用于灾害监测与能源开采。
中文摘要 AI 辅助
NASA Black Marble产品套件中的夜间灯光捕捉了包括火灾、火山喷发和天然气燃烧在内的异常事件的热辐射和光发射信号。现有检测方法主要依赖热红外波段，限制了其对较弱信号的敏感性。我们提出了一种新颖的机器学习框架，该框架联合建模Black Marble M波段和昼夜波段（DNB）信号，以推导出广义的、时空稳健的异常检测器集成。该框架迭代构建检测器，使其可扩展至不同区域、季节、异常类别，并覆盖陆地和海洋。基于相关波段和检测器一致性，推导出不同置信水平的检测集。该方法提高了真实检测率，同时减少了虚假检测，实验结果展示了强大的泛化能力，可应用于自然灾害监测和能源开采领域。
英文摘要
Nighttime lights from NASA's Black Marble product suite capture thermal and light emission signals from anomalous events including fires, volcanic eruptions, and gas flaring. Existing detection approaches rely primarily on thermal bands, limiting sensitivity to weaker signals. We propose a novel machine learning framework that jointly models Black Marble M-band and Day/Night Band (DNB) signals to derive a generalized, spatio-temporally robust ensemble of anomaly detectors. The framework iteratively builds detectors that scale across regions, seasons, anomaly classes, and extends over land and ocean. Detection sets at varying confidence levels are derived based on relevant bands and detector agreement. The approach improves true detection rate while reducing spurious detections and results demonstrate strong generalizability with applications in natural hazard monitoring and energy extraction.
发表机构
- Universities Space Research Association（大学空间研究协会）
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