论文标题
深层网络方法用于多阶段云检测
A deep network approach to multitemporal cloud detection
论文作者
论文摘要
我们提出了一个具有时间记忆的深度学习模型，以检测由安装在MeteoSat第二代（MSG）卫星上的Seviri成像器获得的图像时间序列中。该模型提供具有相关置信度的像素级云图，并通过经常性的神经网络结构及时传播信息。借助单个型号，我们能够全年和白天和黑夜都以高度准确地概述云。
We present a deep learning model with temporal memory to detect clouds in image time series acquired by the Seviri imager mounted on the Meteosat Second Generation (MSG) satellite. The model provides pixel-level cloud maps with related confidence and propagates information in time via a recurrent neural network structure. With a single model, we are able to outline clouds along all year and during day and night with high accuracy.
