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Evaluation of oil thickness by neural network analysis of IR imagery

机译:红外图像神经网络分析对油厚度的评价

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Neural network analysis of conventional thermal infra-red data gathered from surveillance aircraft was undertaken in order to evaluate if this approach can be used to determine the thickness of oil at sea. A Multi-Layer Perceptron meural netowrk architecture was sued to examine sea trial data and incidated that the best configuration for the prediction of oil thickness used the following core input variables: oil brightness, time of day, sea brightness, wind speed, oil type and sea temperature.
机译:采用监控飞机收集的传统热红外数据的神经网络分析,以评估这种方法是否可用于确定海上油的厚度。多层的Perceptron Meural Netowrk架构被起诉架构检查海上试验数据并归因于预测油厚度的最佳配置使用以下核心输入变量:油亮度,一天时间,海亮度,风速,油型和海温。

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