文献类型：期刊文献

英文题名：Multi-task deep learning for quantifying methane emissions from 2-D plume imagery with Low Signal-to-Noise Ratio

作者：Xu, Qianhui[1];Gu, Xiaojing[1];Li, Pengfei[2];Gu, Xingsheng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Chinese Acad Sci, Shanghai Inst Tech Phys, China State Key Lab Infrared Phys, Shanghai, Peoples R China

年份：2025

卷号：46

期号：2

起止页码：704

外文期刊名：INTERNATIONAL JOURNAL OF REMOTE SENSING

收录：;EI(收录号：20244717380605);WOS:【SCI-EXPANDED(收录号:WOS:001349293400001)】；

基金：The work was supported by the National Natural Science Foundation of China [No. 61973122].

语种：英文

外文关键词：Methane emission; Methane quantification; Signal-to-Noise Ratio (SNR); Deep learning; Multi-task learning

摘要：Methane is the second most significant greenhouse gas after carbon dioxide. As global warming intensifies, the quantification of methane point sources is becoming increasingly crucial. However, retrieving high-quality methane signals from remote sensing data remains challenging due to various factors, including surface reflectance, atmospheric interference, sensor noise, wind speed, and sensor sensitivity. In real-world scenarios, methane remote sensing quantification often encounters unfavourable conditions that lead to low signal-to-noise ratio (SNR) signals, resulting in reduced quantification accuracy. To address these challenges, we introduce a novel multi-task learning-based approach. Specifically, we incorporate a denoising auxiliary task into the quantification network by introducing an additional denoising branch that recovers clean plume column concentration maps. The network is trained with supervision using both denoising and quantification losses, which enables it to acquire robust feature representations to noise and benefits the quantification of low SNR images. During the inference phase, the denoising branch is removed, resulting in an efficient and robust single quantification network with reduced inferring time, supporting onboard computation. We construct a low SNR database based on the AVIRIS-NG sensor and evaluate the generalization ability of our method. DQNet achieves the highest RMSE, MAPE, and R of 16.478 kg/h, 15.296%, and 95.167% respectively on the synthetic dataset. Across the entire range of SNR, DQNet exhibits a greater relative advantage as SNR decreases. However, our approach is not limited to AVIRIS-NG and can be easily extended to various multispectral and hyperspectral satellites.