Robust Small Methane Plume Segmentation in Satellite Imagery
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912549032689664 |
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| author | Tran, Khai Duc Minh Van Nguyen, Hoa Rawi, Aimuni Binti Muhammad Athinarayanarao, Hareeshrao Vo, Ba-Ngu |
| author_facet | Tran, Khai Duc Minh Van Nguyen, Hoa Rawi, Aimuni Binti Muhammad Athinarayanarao, Hareeshrao Vo, Ba-Ngu |
| contents | This paper tackles the challenging problem of detecting methane plumes, a potent greenhouse gas, using Sentinel-2 imagery. This contributes to the mitigation of rapid climate change. We propose a novel deep learning solution based on U-Net with a ResNet34 encoder, integrating dual spectral enhancement techniques (Varon ratio and Sanchez regression) to optimise input features for heightened sensitivity. A key achievement is the ability to detect small plumes down to 400 m2 (i.e., for a single pixel at 20 m resolution), surpassing traditional methods limited to larger plumes. Experiments show our approach achieves a 78.39% F1-score on the validation set, demonstrating superior performance in sensitivity and precision over existing remote sensing techniques for automated methane monitoring, especially for small plumes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16282 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Small Methane Plume Segmentation in Satellite Imagery Tran, Khai Duc Minh Van Nguyen, Hoa Rawi, Aimuni Binti Muhammad Athinarayanarao, Hareeshrao Vo, Ba-Ngu Computer Vision and Pattern Recognition Signal Processing This paper tackles the challenging problem of detecting methane plumes, a potent greenhouse gas, using Sentinel-2 imagery. This contributes to the mitigation of rapid climate change. We propose a novel deep learning solution based on U-Net with a ResNet34 encoder, integrating dual spectral enhancement techniques (Varon ratio and Sanchez regression) to optimise input features for heightened sensitivity. A key achievement is the ability to detect small plumes down to 400 m2 (i.e., for a single pixel at 20 m resolution), surpassing traditional methods limited to larger plumes. Experiments show our approach achieves a 78.39% F1-score on the validation set, demonstrating superior performance in sensitivity and precision over existing remote sensing techniques for automated methane monitoring, especially for small plumes. |
| title | Robust Small Methane Plume Segmentation in Satellite Imagery |
| topic | Computer Vision and Pattern Recognition Signal Processing |
| url | https://arxiv.org/abs/2508.16282 |