Robust Small Methane Plume Segmentation in Satellite Imagery

2025/08/22 by Tran, Khai Duc Minh, Van Nguyen, Hoa, Rawi, Aimuni Binti Muhammad +2
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2508.16282

Abstract

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.

Citations

• Mapping Methane -- The Impact of Dairy Farm Practices on Emissions Through Satellite Data and Machine Learning
• Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation
• SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
• Towards a Guideline for Evaluation Metrics in Medical Image Segmentation
• V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
• Deep Residual Learning for Image Recognition
• U-Net: Convolutional Networks for Biomedical Image Segmentation
• Adam: A Method for Stochastic Optimization
• ImageNet classification with deep convolutional neural networks

Related

• AttMetNet: Attention-Enhanced Deep Neural Network for Methane Plume Detection in Sentinel-2 Satellite Imagery
• Autonomous Detection of Methane Emissions in Multispectral Satellite Data Using Deep Learning
• Unlocking the Potential: Multi-task Deep Learning for Spaceborne Quantitative Monitoring of Fugitive Methane Plumes
• Towards Methane Detection Onboard Satellites
• Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation