发表机构
University of Alberta（阿尔伯塔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MethaneFuse利用多传感器卫星数据，在部分传感器可用时学习异构观测，构建MethaneUnion数据集，显著提升甲烷羽流检测性能，减少误报。
AI 中文摘要
从卫星图像进行甲烷羽流检测受到观测不完整的制约：公共卫星提供互补的空间、光谱和大气证据，但由于重访周期、云层覆盖、采集质量以及排放的瞬态特性，实际羽流案例很少包含完全配对的多传感器测量。大多数基于学习的检测器依赖单传感器输入，尤其是Sentinel-2（S2），导致许多报告的羽流案例无法使用。我们构建了MethaneUnion，这是一个基于Carbon Mapper羽流报告和匹配的S2、Landsat 8/9（L8/9）、EMIT和Sentinel-5P（S5P）观测构建的时间多传感器数据集。基于MethaneUnion，MethaneFuse在部分传感器可用性下从异构卫星观测中学习，无需完整的四传感器测量。MethaneUnion将可用覆盖范围从3,211个有效的S2匹配羽流案例扩展到8,981个具有多传感器观测的报告羽流案例。在代表性的480米设置下，MethaneFuse达到84.87的F1分数和93.62的AUROC，相比最强基线提高了5.65的F1和8.30的AUROC，同时将误报率降低了8.19个百分点。传感器可用性实验表明，当S2可用时，MethaneFuse提高了检测性能，并在S2不可用时将羽流知识迁移到L8/9、EMIT和S5P。这些结果证明了从不完整的异构传感器观测中学习对实际甲烷羽流检测的价值。
英文摘要
Methane plume detection from satellite imagery is constrained by incomplete observations: public satellites provide complementary spatial, spectral, and atmospheric evidence, but real plume cases rarely contain fully paired multi-sensor measurements because of revisit schedules, cloud coverage, acquisition quality, and the transient nature of emissions. Most learning-based detectors rely on single-sensor inputs, especially Sentinel-2 (S2), leaving many reported plume cases unusable. We construct MethaneUnion, a temporal multi-sensor dataset built from Carbon Mapper plume reports and matched S2, Landsat 8/9 (L8/9), EMIT, and Sentinel-5P (S5P) observations. Built on MethaneUnion, MethaneFuse learns from heterogeneous satellite observations under partial sensor availability without requiring complete four-sensor measurements. MethaneUnion expands usable coverage from 3,211 valid S2-matched plume cases to 8,981 reported plume cases with multi-sensor observations. At the representative 480 m setting, MethaneFuse achieves 84.87 F1 and 93.62 AUROC, improving over the strongest baseline by 5.65 F1 and 8.30 AUROC points while reducing false positives by 8.19 points. Sensor-availability experiments show that MethaneFuse improves detection when S2 is available and transfers plume knowledge to L8/9, EMIT, and S5P when S2 is unavailable. These results demonstrate the value of learning from incomplete heterogeneous sensor observations for practical methane plume detection.
