FLAME: Physics-Guided Neural Operators for Onboard Satellite Methane Detection in Hyperspectral Imagery

2026-05-31

AI Summary

This work addresses the challenges of high computational cost and insufficient accuracy in deep learning–based detection of methane emission sources from spaceborne hyperspectral imagery. To this end, we propose FLAME, a physics-guided lightweight neural operator that explicitly embeds physical priors of methane absorption into its architecture—a first in this domain. FLAME achieves significantly improved detection accuracy and markedly reduced false alarm rates with minimal model parameters, thereby satisfying the stringent real-time constraints of onboard satellite hardware. Experimental results demonstrate that FLAME attains state-of-the-art accuracy on standard methane detection benchmarks, reducing pixel-level false positives by nearly threefold compared to the strongest neural baseline while maintaining the lowest parameter count, thus effectively balancing precision and efficiency.

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.

Problem

• methane detection
• hyperspectral imagery
• onboard satellite processing
• neural operators
• climate change

Innovation

• physics-guided neural operator
• onboard methane detection
• hyperspectral imagery
• neural operator
• methane absorption physics