ABSTRACT Pattern recognition methodology was developed for the automated detection of methanol vapour plumes from passive multispectral infrared remote sensing data. The data employed in this work were collected with an infrared line scanner equipped with eight spectral bandpass filters mounted in a downward-looking mode on a fixed-wing aircraft. An automated classifier was developed by the application of a backpropagation neural network to the calibrated, registered, and preprocessed radiances. Preprocessing steps were performed to optimize the inputs for the neural network, which included: (1) contrast enhancement by calculating the ratios of band intensities; (2) assembly of training data by use of the -means clustering algorithm; (3) removal of temperature information from the measured radiance data; (4) feature extraction for identifying the most representative ratios of band intensities; and (5) optimization of the starting assumed emissivity value for the temperature and emissivity separation algorithm. The best classifier achieved an overall classification accuracy of 98.07% on the testing set with a false detection rate of 0.90% and a missed detection rate of 13.50%. The prediction performance of the optimized neural network was demonstrated through its application to 16 images not included in the training procedure.

中文翻译：

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用于从机载被动红外多光谱成像数据中自动检测甲醇蒸汽的神经网络

摘要 模式识别方法是为从被动多光谱红外遥感数据自动检测甲醇蒸气羽流而开发的。在这项工作中使用的数据是用红外线扫描仪收集的，该扫描仪配备有八个光谱带通滤波器，以向下看的方式安装在固定翼飞机上。通过将反向传播神经网络应用于校准、配准和预处理的辐射，开发了一种自动分类器。执行预处理步骤以优化神经网络的输入，其中包括：（1）通过计算带强度的比率来增强对比度；(2) 使用-means聚类算法组装训练数据；(3) 从测量的辐射数据中去除温度信息；(4)特征提取，用于识别最具代表性的波段强度比；(5) 温度和发射率分离算法的起始假定发射率值的优化。最佳分类器在测试集上的总体分类准确率为 98.07%，误检率为 0.90%，漏检率为 13.50%。优化后的神经网络的预测性能通过将其应用于未包含在训练过程中的 16 张图像来证明。90%，漏检率为 13.50%。优化后的神经网络的预测性能通过将其应用于未包含在训练过程中的 16 张图像来证明。90%，漏检率为 13.50%。优化后的神经网络的预测性能通过将其应用于未包含在训练过程中的 16 张图像来证明。