FUMESNet: Exploring Frequency-Based Transformer and Improving Skip Connection for Hyperspectral Methane Plume Segmentation

Accurate segmentation of methane (CH4) plumes from hyperspectral imagery (HSI) plays an important role in emission monitoring and environmental assessment. CH4 plume signatures appear as subtle spatial intensity variations accompanied by characteristic spectral responses across specific wave-length ranges, making their delineation challenging under complex background and atmospheric conditions. Spatial-frequency analysis can enhance structural and textural representations of plume regions, helping to suppress background clutter and preserve fine boundary details. However, many existing segmentation approaches do not explicitly exploit spatial-frequency information within the attention mechanism, and encoder-decoder architectures with skip connections often propagate redundant features due to high inter-band correlation, degrading plume-background separation. To address these limitations, we proposeFUMESNet(FreqUency-based transforMer and improving skip connEction for hyperSpectral methaNe plumEsegmenTation), a transformer-based framework that jointly leverages spatial context, spatial-frequency representations, and channel-wise feature relevance for CH4plume segmentation. Specifically, anAdaptive Spatial-Frequency Integration(ASFI) module incorporates magnitude and phase information derived from spatial-frequency analysis into the attention computation, improving the delineation of weak and diffuse plume structures while preserving boundary integrity. In addition, anEfficient Global Channel Attention(EGCA) module is embedded within skip connections to adaptively emphasize spectrally informative and spatially relevant channels, mitigating the influence of redundant features during feature fusion. Experiments on multiple HSI datasets demonstrate thatFUMESNetachieves consistent performance improvements over state-of-the-art (SOTA) methods, and ablation studies further confirm the complementary contributions of ASFI and EGCA to CH4plume segmentation.