FLAME：物理引导的神经算子用于高光谱图像中星载甲烷检测
FLAME: Physics-Guided Neural Operators for Onboard Satellite Methane Detection in Hyperspectral Imagery
AI总结：
提出FLAME，一种将甲烷吸收物理直接嵌入架构的物理引导神经算子，在星载甲烷检测中实现最高精度，像素级假阳性率降低近3倍，参数最少且满足星载硬件延迟预算。
摘要：
甲烷是近期气候变化的主要驱动因素，快速识别其排放源是一项关键的气候干预措施。星载高光谱成像是完成此任务的主要工具，但每个传感器产生的数据量使得地面检测不切实际，因此需要星载检测。经典方法在星载硬件上产生过高的计算成本，而深度学习模型速度快但检测质量不足。我们提出FLAME，一种物理引导的神经算子，将甲烷吸收的物理直接构建到其架构中。在甲烷检测基准上，FLAME在所有评估方法中实现了最高的检测精度，将像素级假阳性率相比最强神经基线降低了近3倍，在学习基线中使用参数最少，并且在星载卫星硬件的延迟预算内运行。
英文摘要：
Methane is a major driver of near-term climate change, and rapidly identifying its emission sources is a critical climate intervention. Spaceborne hyperspectral imagery is the primary tool for this task, but the volume of data produced by each sensor makes ground-based detection impractical and necessitates onboard detection. Classical methods incur prohibitive computational cost on onboard hardware, while deep learning models are fast but fall short on detection quality. We propose FLAME, a physics-guided neural operator that builds the physics of methane absorption directly into its architecture. On the methane detection benchmark, FLAME achieves the highest detection accuracy among all evaluated methods, reduces the pixel-level false positive rate by nearly $3\times$ over the strongest neural baseline, uses the fewest parameters among learned baselines, and runs within the latency budget of onboard satellite hardware.
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