Multi-Platform Methane Plume Detection via Model and Domain Adaptation

TL;DR

This paper presents machine learning and domain adaptation techniques to improve methane plume detection across different remote sensing platforms, enhancing detection accuracy by aligning data distributions from airborne and spaceborne sensors.

Contribution

It introduces a novel combination of transfer learning and CycleGAN-based data alignment to enhance cross-platform methane detection performance.

Findings

Transfer learning improves spaceborne methane classifier accuracy.

CycleGAN effectively aligns airborne and spaceborne data distributions.

Applying airborne classifiers to aligned spaceborne data yields superior detection results.

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.