Frequency and Spatial Domain Injection Network for Methane Plumes Semantic Segmentation

Methane, a powerful greenhouse gas, significantly influences climate change. Estimating methane emission fluxes is critical for implementing emission reduction strategies, and plumes segmentation is an important technique for accurate quantification. However, existing methane plumes segmentation studies focus on pixel-level localization of spatial and spectral features, which makes it difficult to capture global contextual information of remote sensing images. To tackle these challenges, we propose a novel frequency and spatial domain injection network (FSDINet) for methane plumes segmentation. FSDINet introduces three blocks that process spectral features utilizing the frequency domain to capture global contextual information and supplement local information with pixel values in the spatial domain, thus allowing for precise localization and segmentation. We conduct extensive experiments on hyperspectral and multispectral datasets. On the Airborne Visible InfraRed Imaging Spectrometer—Next Generation (AVIRIS-NG) hyperspectral dataset, the majority of our model evaluation scores exceed those of existing methods in a range of conditions, including both strong and weak plumes, with the highest precision and intersection over union (IOU) of 87.75% and 65.56%, respectively. On the WorldView-3 multispectral dataset, precision and IOU improved up to 8.76% and 4.04% over methods utilizing spatial and spectral features. Furthermore, the zero-shot experiments on the Earth Surface Mineral Dust Source Investigation (EMIT) dataset confirm the robust generalization capacity of our model when confronted with different sensor data.