直接在未校正影像上训练模型，实现卫星实时甲烷泄漏检测
Methane Detection On Board Satellites from Unorthorectified Imagery
- 跳过传统几何校正和滤波步骤，直接用未校正高光谱图像训练模型
- 模型在所有泄漏源上达到16.91%的交并比，接近校正后模型的18.47%
- 支持星上部署，量化压缩后模型体积减半，精度损失小于0.3%
甲烷是强效温室气体，有效减排依赖及时探测。传统方法依赖几何校正与匹配滤波增强泄漏信号，但这些步骤适用于地面处理，不适用于星上运行。本文提出UnorthoDOS数据集与方法，直接在未校正的高光谱影像上训练机器学习模型，跳过校正与滤波环节。基于未校正数据训练的U-Net模型在所有泄漏源上取得16.91%的交并比，接近校正数据训练模型的18.47%，均显著优于基准的mag1c匹配滤波方法（4.76%）。进一步验证了星上部署可行性：使用FP16量化压缩后模型体积减半，输出偏差低于0.3%。所训练模型及两个机器学习可用的数据集——来自EMIT传感器的校正与未校正高光谱影像——已公开于https://huggingface.co/datasets/SpaceML/UnorthoDOS，代码见https://github.com/spaceml-org/plume-hunter。
原文摘要 · Abstract (English)
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.
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