This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the 3.7 μm and 10.8 μm channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation–maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

中文翻译：

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无监督学习在夜间海雾检测中的新应用。

本文提出了一种结合无监督学习技术的夜间海雾检测算法。该算法基于数据集，该数据集将通信，海洋和气象卫星（COMS）上的气象成像仪（MI）的3.7μm和10.8μm通道的亮度温度与操作性海面温度和海面的海面温度相结合冰分析（OSTIA）。先前的算法通常采用阈值，包括近红外和红外之间的亮度温度差。该阈值先前是根据气候分析或模型模拟确定的。尽管使用预定阈值的此方法对于检测低云非常简单有效，它很难区分雾和地层，因为它们具有相似的粒径和高度特征。为了改善这一点，已经采用了无监督学习方法，该方法可以从不足的信息中进行更有效的解释。本文采用的无监督学习方法是期望最大化（EM）算法，该算法广泛用于不完全数据问题。它通过组织和优化数据来识别数据的显着特征。通过考虑特定域的特性，可以为雾检测应用最佳阈值。已使用具有正交偏振（CALIOP）垂直剖面积云的气溶胶激光雷达对算法进行了评估，