Toward on-board methane detection in hyperspectral images

Detecting methane in satellite hyperspectral images (HSIs) can play a key role in environmental monitoring, as taking timely actions to reduce its emission and handle (unexpected) super emitters is of paramount importance. We tackle this issue and propose a machine learning pipeline for this task, with the ultimate goal of deploying it on board a satellite. Such solutions can offer global scalability, and they can act as a smart data prioritization step, as only those HSIs which contain methane can be downlinked for further analysis. However, the on-board deployment induces additional practical challenges-such algorithms should be resource-frugal, and should effectively operate on the target image data which may not be available during their development, since the satellite is not in orbit yet. Our experimental study revealed that the data-driven approaches can effectively detect methane in original airborne HSIs, as well as in HSIs emulating the target sensor and generated through data-level simulations.