基于稀疏先验的成像光谱数据甲烷浓度快速准确反演
Fast and Accurate Retrieval of Methane Concentration from Imaging Spectrometer Data Using Sparsity Prior
摘要：
大气甲烷强烈的辐射强迫激发了人们对确定这种强效温室气体的自然和人为来源的兴趣。点源是重要的量化目标，人为目标具有减排潜力。甲烷点源羽流探测和浓度反演以前已经证明使用的数据从机载可见光红外成像光谱仪下一代（AVIRIS-NG）。目前的定量方法在计算要求和检索精度之间进行权衡，为处理来自飞行活动的实时数据或大型数据集造成了障碍。我们提出了一种新的计算效率高的算法，适用于稀疏性和一个多校正的匹配滤波器检索的痕量气体浓度的路径长度。新的算法进行了测试，使用AVIRIS-NG数据采集的几个点源羽在印度艾哈迈达巴德。该算法进行了验证，使用模拟AVIRIS-NG数据，包括已知甲烷浓度的合成羽流。稀疏性和多校正一起减少了60.7%的根均方误差检索甲烷浓度的路径长度增强相比，以前的强大的匹配滤波器方法。背景噪声降低了2.64倍。新算法能够在台式计算机上通过GPU加速在8小时内处理整个300飞行路线2016 AVIRIS-NG印度活动。
英文摘要：
The strong radiative forcing by atmospheric methane has stimulated interest in identifying natural and anthropogenic sources of this potent greenhouse gas. Point sources are important targets for quantification, and anthropogenic targets have potential for emissions reduction. Methane point source plume detection and concentration retrieval have been previously demonstrated using data from the Airborne Visible InfraRed Imaging Spectrometer Next Generation (AVIRIS-NG). Current quantitative methods have tradeoffs between computational requirements and retrieval accuracy, creating obstacles for processing real-time data or large datasets from flight campaigns. We present a new computationally efficient algorithm that applies sparsity and an albedo correction to matched filter retrieval of trace gas concentration-pathlength. The new algorithm was tested using AVIRIS-NG data acquired over several point source plumes in Ahmedabad, India. The algorithm was validated using simulated AVIRIS-NG data including synthetic plumes of known methane concentration. Sparsity and albedo correction together reduced the root mean squared error of retrieved methane concentration-pathlength enhancement by 60.7% compared with a previous robust matched filter method. Background noise was reduced by a factor of 2.64. The new algorithm was able to process the entire 300 flightline 2016 AVIRIS-NG India campaign in just over 8 hours on a desktop computer with GPU acceleration.
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