FLAME: Physics-Guided Neural Operators for Onboard Satellite Methane Detection in Hyperspectral Imagery
2026-06-01 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors focus on detecting methane, a gas that speeds up climate change, using satellite images. Traditional methods are too slow for use directly on satellites, and existing fast methods don’t detect methane well enough. They created FLAME, a new method that includes methane’s physical properties in its design. This approach makes methane detection more accurate, reduces errors, and works efficiently on satellite computers.
Methane detectionHyperspectral imagerySatellite onboard processingPhysics-guided neural networksNeural operatorDeep learningFalse positive rateClimate changeRemote sensingComputational efficiency
Authors
Junhyuk Heo, Junhwan Park, Sancheol Sim, Beomkyu Choi, Woojin Cho
Abstract
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.