Fast Cloud Segmentation Using Convolutional Neural Networks
Fast Cloud Segmentation Using Convolutional Neural Networks
复制标题
DOI：
10.3390/rs10111782
复制
发表时间：
2018-11-01
期刊：
影响因子：
5
通讯作者：
Seeger, Bernhard
中科院分区：
文献类型：
--
作者：
Droener, Johannes;Korfhage, Nikolaus;Seeger, Bernhard
关于云的信息对于观测和预测天气和气候以及太阳能发电和分配都很重要。大多数现有方法通过对单个像素进行分类来提取卫星数据中的云信息，而不是使用紧密集成的空间信息，忽略了云是高度动态的、空间连续的实体这一事实。提出了一种基于深度学习的云分类方法。基于卷积神经网络（CNN）的图像分割架构，本文提出的云分割CNN （CS-CNN）算法可以同时对场景的所有像素进行分类，而不是单独进行分类。研究表明，CS-CNN可以成功地处理多光谱卫星数据，对高动态云等连续现象进行分类。与随机森林等其他机器学习方法相比，所提出的方法在Meteosat第二代（MSG）卫星数据上的质量、鲁棒性和运行时间方面都取得了出色的结果。特别是，将CS-CNN与基于MSG数据的CLAAS-2云掩模进行比较，显示出较高的准确率（0.94）和Heidke Skill Score（0.90）值。与随机森林相比，CS-CNN产生了强大的结果，并且对海岸线和明亮（沙）表面区域造成的挑战不敏感。使用GPU加速，CS-CNN对欧洲508 × 508像素的图像进行分类，计算时间仅为25 ms。
Information about clouds is important for observing and predicting weather and climate as well as for generating and distributing solar power. Most existing approaches extract cloud information from satellite data by classifying individual pixels instead of using closely integrated spatial information, ignoring the fact that clouds are highly dynamic, spatially continuous entities. This paper proposes a novel cloud classification method based on deep learning. Relying on a Convolutional Neural Network (CNN) architecture for image segmentation, the presented Cloud Segmentation CNN (CS-CNN), classifies all pixels of a scene simultaneously rather than individually. We show that CS-CNN can successfully process multispectral satellite data to classify continuous phenomena such as highly dynamic clouds. The proposed approach produces excellent results on Meteosat Second Generation (MSG) satellite data in terms of quality, robustness, and runtime compared to other machine learning methods such as random forests. In particular, comparing CS-CNN with the CLAAS-2 cloud mask derived from MSG data shows high accuracy (0.94) and Heidke Skill Score (0.90) values. In contrast to a random forest, CS-CNN produces robust results and is insensitive to challenges created by coast lines and bright (sand) surface areas. Using GPU acceleration, CS-CNN requires only 25 ms of computation time for classification of images of Europe with 508 x 508 pixels.