Methane-Plume Segmentation From Hyperspectral Satellite Imagery Via Multimodal Deep Learning
Quick Answer
This paper shows that A multimodal deep learning model with a feature-guided methane enhancement mechanism achieves superior methane plume segmentation on the MPDataset, improving MIoU by +0.92, MPrecision by +0.87, and Recall by +1.01, while maintaining lower computational costs compared to existing architectures.
Key Points
- Proposed model integrates feature-guided methane enhancement for improved segmentation.
- Achieved state-of-the-art performance on MPDataset with significant metric improvements.
- Lower computational cost enables efficient large-scale methane monitoring.
- Highlights potential of multimodal fusion strategies in remote sensing applications.
Paper Resources
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~2 min readAbstract: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.
| Comments: | Accepted at IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2026 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2606.26416 [cs.CV] |
| (or arXiv:2606.26416v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2606.26416 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Brayan Quintero [view email]
[v1]
Wed, 24 Jun 2026 22:12:16 UTC (3,700 KB)
— Originally published at arxiv.org
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