对比传统特征与深度学习模型，提升卫星甲烷泄漏识别准确率
Explainable Comparison of Feature-Based and Deep Learning Models for TROPOMI Methane Plume Screening
- 用图像和特征两种方法分类甲烷泄漏信号与伪影
- 深度学习模型在不平衡数据下表现更优，准确率达89.2%
- 通过可解释性分析，帮助理解模型决策依据，适合运维系统参考
持续、全球范围检测大型甲烷排放是减缓全球变暖的关键步骤。卫星观测（如S5P/TROPOMI）结合泄漏检测算法在此过程中发挥重要作用。然而，并非所有看似甲烷泄漏的云团都是真实排放所致；大量类似云团实为反演伪影，可能由高程变化、反照率梯度、气溶胶浓度高、海岸线或水体等引起。以往研究采用支持向量机（SVC）对领域专家设计的观测特征进行分类，但该方法限制了信息范围，破坏像素空间关系，并在统计聚合中损失细节。本研究在平衡与不平衡设置下，对比了基于特征的模型（SVC、随机森林、XGBoost）与基于图像的模型（ResNet-18、ResNet-34）在甲烷泄漏-伪影分类中的表现。通过SHAP可解释性分析，揭示两类模型的决策机制。结果为实际应用如CAMS甲烷热点探测器提供选型指导。
原文摘要 · Abstract (English)
Continuous and global detection of large methane emissions is a crucial step for global warming mitigation. Satellite observations, such as from S5P/TROPOMI, combined with plume detection algorithms, can play a key role in this effort. However, not all TROPOMI plume detections that look like methane emission plumes are the result of actual emissions. A significant part of the plume-like features in the data are retrieval artifacts. Such artifacts could be the result of variations in elevation or albedo gradients, high concentrations of aerosols, coastal lines, water bodies, etc. Previous work approached the problem of plume-artifact classification by means of a Support Vector Machine Classifier (SVC), trained on an extensive set of observation-based scalar features designed by domain experts. However, such an approach limits the information scope received by the algorithm to what is deemed to be important by the experts, breaks the spatial relationship between pixels, and loses information during the process of statistical aggregation. In this study, we compare feature-based (SVC, Random Forest, XGBoost) and image-based (ResNet-18, ResNet-34) models for methane plume-artifact classification under balanced and imbalanced evaluation settings. To interpret the results, we apply SHAP-based explainability to both model families. Our findings provide practical guidance for model selection in operational methane-screening workflows such as the CAMS Methane Hotspot Explorer.
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