用多模态深度学习提升卫星图像甲烷泄漏精准分割效率
Methane-Plume Segmentation From Hyperspectral Satellite Imagery Via Multimodal Deep Learning
- 融合物理先验的增强机制，分层注入甲烷特征到视觉表征
- 在MPDataset上实现MIoU提升0.92，精度与召回率同步提高
- 计算成本更低，适合大规模实时监测应用
高效检测甲烷排放对理解与缓解全球变暖至关重要。准确识别并分割地球观测图像中的甲烷羽流仍是大规模监测的核心挑战。本文提出一种多模态深度学习模型，集成特征引导的甲烷增强（FGME）机制，在多个语义层级将具有物理意义的甲烷线索注入基于Transformer的RGB表示中。在MPDataset上的实验表明，该方法优于现有最先进模型，分别实现MIoU提升0.92、精确率提升0.87、召回率提升1.01。值得注意的是，这些性能提升以显著更低的计算开销达成，展现出优异的准确性-效率权衡，适用于真实遥感场景下的可扩展甲烷羽流分割。
原文摘要 · Abstract (English)
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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