Multi-Platform Methane Plume Detection via Model and Domain Adaptation
Journal:
arXiv
Published Date:
Jun 2, 2025
Abstract
Prioritizing methane for near-term climate action is crucial due to its
significant impact on global warming. Previous work used columnwise matched
filter products from the airborne AVIRIS-NG imaging spectrometer to detect
methane plume sources; convolutional neural networks (CNNs) discerned
anthropogenic methane plumes from false positive enhancements. However, as an
increasing number of remote sensing platforms are used for methane plume
detection, there is a growing need to address cross-platform alignment. In this
work, we describe model- and data-driven machine learning approaches that
leverage airborne observations to improve spaceborne methane plume detection,
reconciling the distributional shifts inherent with performing the same task
across platforms. We develop a spaceborne methane plume classifier using data
from the EMIT imaging spectroscopy mission. We refine classifiers trained on
airborne imagery from AVIRIS-NG campaigns using transfer learning,
outperforming the standalone spaceborne model. Finally, we use CycleGAN, an
unsupervised image-to-image translation technique, to align the data
distributions between airborne and spaceborne contexts. Translating spaceborne
EMIT data to the airborne AVIRIS-NG domain using CycleGAN and applying airborne
classifiers directly yields the best plume detection results. This methodology
is useful not only for data simulation, but also for direct data alignment.
Though demonstrated on the task of methane plume detection, our work more
broadly demonstrates a data-driven approach to align related products obtained
from distinct remote sensing instruments.
