用物理规律提升卫星遥感甲烷检测精度与效率
FLAME: Physics-Guided Neural Operators for Onboard Satellite Methane Detection in Hyperspectral Imagery
- 将甲烷吸收物理特性嵌入神经网络架构
- 误报率降低近3倍，精度优于所有现有方法
- 模型轻量且满足星上实时计算需求
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关键气候干预手段。空间超光谱成像为此任务主要工具，但单个传感器数据量巨大，地面检测不现实，需星上实时处理。传统方法在星上硬件上计算成本过高，深度学习虽快但检测质量不足。本文提出FLAME，一种将甲烷吸收物理特性直接融入架构的物理引导神经算子。在甲烷检测基准测试中，FLAME在所有评估方法中达到最高检测精度，相比最强神经基线，像素级误报率降低近3倍，参数量最少，且运行延迟满足星上硬件时延预算。
原文摘要 · Abstract (English)
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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