MethaneMapper：用于甲烷检测的光谱吸收感知高光谱Transformer
MethaneMapper: Spectral Absorption aware Hyperspectral Transformer for Methane Detection
AI总结：
提出端到端光谱吸收波长感知Transformer网络MethaneMapper用于检测和量化甲烷排放，构建了超4000个羽流位点的大规模数据集，检测精度达0.63 mAP且模型体积缩小5倍。
摘要：
甲烷（CH$_4$）是全球气候变化的主要促成因素。最近的机载可见光-红外成像光谱仪-新一代（AVIRIS-NG）在甲烷排放的定量制图方面非常有用。分析此数据的现有方法对局部地形条件敏感，通常需要领域专家的人工检查，容易出现重大误差，因此不可扩展。为了应对这些挑战，我们提出了一种新颖的端到端光谱吸收波长感知transformer网络MethaneMapper，用于检测和量化排放。MethaneMapper引入了两个新颖模块，有助于在光谱域中定位最相关的甲烷羽流区域，并利用它们准确进行空间定位。全面评估表明，MethaneMapper在检测中实现了0.63 mAP，并且与当前最先进技术相比，模型尺寸减小了5倍。此外，我们还引入了一个大规模甲烷羽流分割掩码数据集，包含2015至2022年间超过1200条AVIRIS-NG飞行线。它包含超过4000个甲烷羽流站点。我们的数据集将为研究人员提供机会，开发和推进新方法来解决这一具有重大广泛社会影响的极具挑战性的温室气体检测问题。数据集和源代码已公开。
英文摘要：
Methane (CH$_4$) is the chief contributor to global climate change. Recent Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) has been very useful in quantitative mapping of methane emissions. Existing methods for analyzing this data are sensitive to local terrain conditions, often require manual inspection from domain experts, prone to significant error and hence are not scalable. To address these challenges, we propose a novel end-to-end spectral absorption wavelength aware transformer network, MethaneMapper, to detect and quantify the emissions. MethaneMapper introduces two novel modules that help to locate the most relevant methane plume regions in the spectral domain and uses them to localize these accurately. Thorough evaluation shows that MethaneMapper achieves 0.63 mAP in detection and reduces the model size (by 5x) compared to the current state of the art. In addition, we also introduce a large-scale dataset of methane plume segmentation mask for over 1200 AVIRIS-NG flight lines from 2015-2022. It contains over 4000 methane plume sites. Our dataset will provide researchers the opportunity to develop and advance new methods for tackling this challenging green-house gas detection problem with significant broader social impact. Dataset and source code are public
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
