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Open AccessArticle

Fast Cloud Segmentation Using Convolutional Neural Networks

Department of Mathmatics and Computer Science, University of Marburg, 35043 Marburg, Germany
Laboratory for Climatology and Remote Sensing, University of Marburg, 35037 Marburg, Germany
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(11), 1782;
Received: 20 October 2018 / Revised: 5 November 2018 / Accepted: 7 November 2018 / Published: 10 November 2018
(This article belongs to the Special Issue Deep Learning for Remote Sensing)
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 × 508 pixels. View Full-Text
Keywords: Meteosat Second Generation; Convolutional Neural Networks; Cloud Mask Meteosat Second Generation; Convolutional Neural Networks; Cloud Mask
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MDPI and ACS Style

Drönner, J.; Korfhage, N.; Egli, S.; Mühling, M.; Thies, B.; Bendix, J.; Freisleben, B.; Seeger, B. Fast Cloud Segmentation Using Convolutional Neural Networks. Remote Sens. 2018, 10, 1782.

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