液晶与显示2026，Vol.41Issue(3)：415-429,15.DOI:10.37188/CJLCD.2026-0017
基于羽流形态约束与特征解耦的弱甲烷羽流高光谱遥感分割方法
High-spectral remote sensing segmentation method for weak methane plumes based on plume morphology constraints and feature decoupling
摘要
Abstract
Methane(CH4)is the second largest anthropogenic greenhouse gas after carbon dioxide.Rapid and accurate identification of methane emission sources is of great significance for climate change monitoring.With the deepening concern over climate change issues,the application of hyperspectral remote sensing technology in methane plume monitoring has gradually become a research hotspot.However,existing studies mostly focus on large-scale methane emission events,while paying insufficient attention to dense and weak methane plumes.Weak methane plumes often fail to present complete plume shapes and have blurred boundary features in hyperspectral images,and are more susceptible to interference from multiple factors such as the atmosphere,surface,and instrument response.To address the problems of blurred boundaries and susceptibility to background noise in weak methane plumes,this paper proposes a deep learning model called LightMethaneNet(LM-Net).LM-Net improves the existing deep learning models for methane plume detection by introducing a dynamic Gaussian module(DGMM)to enhance the spatial features of the plume and a hybrid attention mechanism to refinie features and suppressing background noise.This solves the problem that existing deep learning-based segmentation methods have difficulty balancing accuracy and efficiency when dealing with such gas plumes.The proposed method was trained and evaluated on the public dataset STARCOP,achieving 53%accuracy and 82.7%recall rate for the detection of weak plumes.Compared with classic segmentation models,the maximum improvements of LM-Net in mIoU,F1 score,and precision were 32.34%,38.01%,and 34.57%,respectively.This method effectively improves the detection accuracy of weak methane plumes,enabling better identification of weak plumes and their suspected emission areas,and provides a feasible technical solution for high-precision methane plume remote sensing detection under complex backgrounds.关键词
甲烷/深度学习/微弱羽流检测/混合注意力机制/动态高斯模块Key words
methane/deep learning/weak plume detection/hybrid attention mechanism/dynamic Gaussian mixture model分类
信息技术与安全科学引用本文复制引用
赵翼,李海巍,张小荣,宋丽瑶,杨伟华,郭琦,李晗之..基于羽流形态约束与特征解耦的弱甲烷羽流高光谱遥感分割方法[J].液晶与显示,2026,41(3):415-429,15.基金项目
陕西省教育厅一般专项科研计划(No.24JK0481) （No.24JK0481）
陕西省自然科学基础研究计划(No.2025JC-YBQN-366,No.2025JC-YBMS-256)Supported by General Special Scientific Research Program of Shaanxi Provincial Department of Education(No.24JK0481) （No.2025JC-YBQN-366,No.2025JC-YBMS-256）
Basic Natural Science Research Program of Shaanxi Province(No.2025JC-YBQN-366,No.2025JC-YBMS-256) （No.2025JC-YBQN-366,No.2025JC-YBMS-256）
