BAYESIAN CLASSIFICATION METHODS FOR CLOUD DETECTION

机译：云检测贝叶斯分类方法

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The cloud detection problem is inherent in all atmospheric and Earth surface applications of satellite data and is fundamentally Bayesian in nature. We apply a rigorous Bayesian framework to the problem and show the advantages of this approach to cloud detection in comparison with existing threshold and neural network based schemes over both land and ocean. Bayesian cloud detection has the potential to be extended to more general image classification and we show here development into to a three-way classifier to identify ocean, sea-ice and cloud at high latitudes. Future developments include aerosol classification and nighttime cloud clearing over land. This approach is an excellent candidate for application to SLSTR data.

机译：云检测问题是卫星数据的所有大气和地球表面应用中所固有的，并且基本上是贝叶斯的自然界。我们将严谨的贝叶斯框架应用于问题，并展示了这种方法对云检测的优势与土地和海洋的现有阈值和神经网络的方案相比。贝叶斯云检测有可能扩展到更全面的图像分类，我们将在此显示开发到三通分类器，以识别高纬度地区的海洋，海冰和云。未来的发展包括空气罗尔分类和夜间云清除土地。这种方法是用于SLST数据的优秀候选者。

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《Sentinel-3 OLCI/SLSTR and MERIS/(A)ATSR workshop》|2012年||共4页
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Claire Bulgin; Stuart MacCallum; Owen Embury; Chris Merchant;
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中图分类航天（宇宙航行）;
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BAYESIAN CLASSIFICATION METHODS FOR CLOUD DETECTION

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