Automated detection of volatile organic compounds in the atmosphere can be achieved by applying pattern recognition analysis to passive infrared (IR) multispectral remote sensing data. However, obtaining analyte-active training data through field experiments is time-consuming and expensive. To address this issue, methodology has been developed for simulating radiance profiles acquired using a multispectral IR line-scanner mounted in a downward-looking position on a fixed-wing aircraft. The simulation strategy used Planck's radiation law and a radiometric model along with the laboratory spectrum of the target compound to compute the upwelling IR background radiance with the presence of the analyte within the instrumental field-of-view. By combining the simulated analyte-active radiances and field-collected analyte-inactive radiances, a synthetic training dataset was constructed. A backpropagation neural network was employed to build classifiers with the synthetic training dataset. Employing methanol as the target compound, the performance of the classifiers was evaluated with field-collected data from airborne surveys at two test fields.

中文翻译：

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基于合成训练数据的神经网络用于被动红外多光谱图像中化学羽流的自动检测

通过对被动红外（IR）多光谱遥感数据应用模式识别分析，可以实现大气中挥发性有机化合物的自动检测。然而，通过现场实验获得分析物活性训练数据既耗时又昂贵。为了解决这个问题，我们开发了一种方法来模拟使用安装在固定翼飞机上向下观察位置的多光谱红外线扫描仪获取的辐射轮廓。模拟策略使用普朗克辐射定律和辐射模型以及目标化合物的实验室光谱来计算仪器视场内存在分析物时的上涌红外背景辐射亮度。通过结合模拟的分析物活性辐射和现场收集的分析物非活性辐射，构建了合成训练数据集。采用反向传播神经网络利用合成训练数据集构建分类器。使用甲醇作为目标化合物，利用两个测试场机载调查现场收集的数据评估分类器的性能。