神经网络支持下的Sentinel-2卫星影像自动云检测

为解决利用Sentinel-2卫星影像进行地物信息提取时云层遮挡造成的信息误判问题，提出了一种基于深度学习的遥感影像云区高精度分割方法。该方法通过预处理的遥感样本数据构建出一种深度神经网络模型，自动提取高层次影像特征；再将影像特征输入分类器，实现遥感影像的像素级分类，从而分割出云覆盖矩阵；最后将云覆盖矩阵转化为云二值图，结合感兴趣区矢量准确获取指定区域云检测结果。选取典型区域进行测试，结果表明：该方法检测精度较高，速度较快，且无须辅助信息与人工干预，可用于Sentinel-2卫星影像不规则区域自动云检测。

Automatic cloud detection of sentinel-2 satellite images based on neural network

This paper proposed a high accuracy segmentation method of remote sensing image cloud region based on deep learning to overcome the problem of misjudgment of information caused by cloud covering when using sentinel-2 satellite image to extract ground object information. This method constructs a deep neural network model through the preprocessed remote sensing sample data, and automatically extracts high-level image features. Then the image features are input into the classifier to realize the pixelwise classification of remote sensing image, and the cloud coverage matrix is segmented. Finally, the cloud coverage matrix is transformed into a cloud binary map, which is combined with the region of interest to accurately obtain the cloud detection results of the designated region. The method will provide a new idea for automatic cloud detection in irregular region of sentinel-2 satellite images without auxiliary information and human intervention.