发表机构
University of Oxford; Delft University of Technology; Universitat de València; University of Surrey; European Space Agency（牛津大学; 代尔夫特理工大学; 瓦伦西亚大学; 萨里大学; 欧洲空间局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出UnorthoDOS方法，利用未正射校正高光谱影像训练U-Net模型检测甲烷，性能接近正射校正模型且优于mag1c基线，经FP16压缩后可适配星上部署，相关数据与代码公开。
AI 中文摘要
甲烷作为一种强效温室气体，是气候变化的主要驱动因素，其有效减排依赖于及时检测。传统检测方法依赖正射校正来纠正几何畸变，以及匹配滤波器来增强羽流信号，这些步骤专为地面处理设计，难以适配星上执行。我们提出UnorthoDOS，这是一个用于直接在未正射校正高光谱影像上训练机器学习模型的数据集与方法，绕过了正射校正和匹配滤波器产品。我们在未正射校正数据上训练的U-Net模型达到了在正射校正数据上训练的模型的性能（所有羽流的IoU分别为16.91%和18.47%），且两者均大幅优于mag1c匹配滤波器基线（IoU为4.76%）。我们进一步证明了星上部署的可行性：FP16压缩将模型大小减半，输出偏差低于0.3%。训练后的机器学习模型以及两个机器学习就绪数据集——来自EMIT传感器的正射校正和未正射校正高光谱影像——可在此httpsURL公开获取，代码在此httpsURL。
英文摘要
As a potent greenhouse gas, methane is a major driver of climate change. Its effective mitigation relies on timely detection. Conventional detection methods rely on orthorectification to correct geometric distortions and matched filters to enhance plume signals, which are steps designed for ground processing and poorly suited to onboard execution. We introduce UnorthoDOS, a dataset and approach for training machine learning models directly on unorthorectified hyperspectral imagery, bypassing both orthorectification and matched-filter products. Our U-Net models trained on unorthorectified data approach the performance of models trained on orthorectified data (IoU 16.91% vs. 18.47% on all plumes), while both substantially outperform the mag1c matched-filter baseline (IoU 4.76%). We further demonstrate the feasibility of onboard deployment: FP16 compression halves model size with under 0.3% output deviation. The trained ML models and two ML-ready datasets -- orthorectified and unorthorectified hyperspectral imagery from the EMIT sensor -- are publicly available at https://huggingface.co/datasets/SpaceML/UnorthoDOS, with code at https://github.com/spaceml-org/plume-hunter.
