Methane-Plume Segmentation From Hyperspectral Satellite Imagery Via Multimodal Deep Learning

• URL: http://arxiv.org/abs/2606.26416v1
• Date: Wed, 24 Jun 2026 22:12:16 GMT
• Title: Methane-Plume Segmentation From Hyperspectral Satellite Imagery Via Multimodal Deep Learning
• Abstract summary: We propose a multimodal deep learning model that integrates a feature-guided methane enhancement mechanism.<n>Our method is evaluated on the MPDataset, where it outperforms the state-of-the-art with improvements of +0.92 in MIoU, +0.87 in MPrecision and +1.01 in Recall.
• Score: 0.11666234644810893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient detection of methane plumes is crucial for understanding and mitigating global warming, as accurately identifying and segmenting them in earth observation imagery remain essential for large-scale monitoring. In this work, we propose a multimodal deep learning model that integrates a feature-guided methane enhancement (FGME) mechanism which injects physically meaningful methane cues into transformer-based RGB representations at multiple semantic scales. Our method is evaluated on the MPDataset, where it outperforms the state-of-the-art with improvements of +0.92 in MIoU, +0.87 in MPrecision and +1.01 in Recall. Notably, these gains are obtained with a substantially lower computational cost than other high-performing architectures, resulting in a favorable accuracy-efficiency trade-off for large-scale methane monitoring. These results highlight the potential of efficient multimodal fusion strategies for accurate and scalable methane plume segmentation in real-world remote sensing applications.

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