长波红外（LWIR）高光谱视频序列数据中气体羽流的检测与跟踪

高光谱图像中改进气体羽流识别的局部背景估计

Local Background Estimation for Improved Gas Plume Identification in Hyperspectral Images

本文提出利用图像分割与迭代背景估计算法为高光谱图像中的气体羽流生成局部背景估计，以解决城市场景背景异质导致的识别性能下降问题，并在模拟与真实数据上验证其优于全局背景估计。

Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data

In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with LWIR data. Measurements from a multi-sensor field campaign represent the benchmark dataset which contains measured hyperspectral, LWIR, and soil-moisture data conducted on grassland site. We introduce a regression framework with three steps consisting of feature selection, preprocessing, and well-chosen regression models. The latter are mainly supervised machine learning models. An exception are the self-organizing maps which combine unsupervised and supervised lea…

基于多模态深度学习的高光谱卫星图像甲烷羽流分割

Methane-Plume Segmentation From Hyperspectral Satellite Imagery Via Multimodal Deep Learning

提出一种多模态深度学习模型，集成特征引导的甲烷增强机制，在MPDataset上取得优于现有方法的性能，实现高精度与高效率的甲烷羽流分割。

基于多维迭代滤波分解的高光谱化学烟雾检测算法

Hyperspectral Chemical Plume Detection Algorithms Based On Multidimensional Iterative Filtering Decomposition

本文提出基于多维迭代滤波分解的后处理工具，用于提升化学烟雾边界识别性能，并通过预处理方法实现高光谱数据的去相关与均值中心化，改进了余弦相似度分类器性能。

Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation

The new generation of hyperspectral imagers, such as PRISMA, has improved significantly our detection capability of methane (CH4) plumes from space at high spatial resolution (30m). We present here a complete framework to identify CH4 plumes using images from the PRISMA satellite mission and a deep learning model able to detect plumes over large areas. To compensate for the relative scarcity of PRISMA images, we trained our model by transposing high resolution plumes from Sentinel-2 to PRISMA. Our methodology thus avoids computationally expensive synthetic plume generation from Large Eddy Simu…

利用EMIT的超光谱辐射测量通过深度学习实现甲烷点源的全球监测

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT

本文提出MAPL-EMIT模型，通过整合光谱和空间信息，利用EMIT仪器的完整辐射光谱数据，实现对甲烷增强的高精度检测，提升全球甲烷点源监测的效率和准确性。

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

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。

Persistent Homology on Grassmann Manifolds for Analysis of Hyperspectral Movies

The existence of characteristic structure, or shape, in complex data sets has been recognized as increasingly important for mathematical data analysis. This realization has motivated the development of new tools such as persistent homology for exploring topological invariants, or features, in large data sets. In this paper we apply persistent homology to the characterization of gas plumes in time dependent sequences of hyperspectral cubes, i.e. the analysis of 4-way arrays. We investigate hyperspectral movies of Long-Wavelength Infrared data monitoring an experimental release of chemical simul…

基于未正射校正影像的星载甲烷检测

Methane Detection On Board Satellites from Unorthorectified Imagery

该研究提出UnorthoDOS方法，利用未正射校正高光谱影像训练U-Net模型检测甲烷，性能接近正射校正模型且优于mag1c基线，经FP16压缩后可适配星上部署，相关数据与代码公开。

释放潜力：用于星载逸散甲烷羽流定量监测的多任务深度学习

Unlocking the Potential: Multi-task Deep Learning for Spaceborne Quantitative Monitoring of Fugitive Methane Plumes

针对星载甲烷羽流定量监测，本文基于LES与RTE构建PRISMA仿真数据集，提出融合浓度反演、羽流分割和排放速率估算的多任务深度学习框架，MTL模型精度优于传统算法和单任务模型。

向卫星 onboard 的甲烷检测

Towards Methane Detection Onboard Satellites

本文提出一种无需预处理的甲烷检测方法，利用未正射校正数据训练模型，与传统方法性能相当，并释放了相关数据集和代码。