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Open AccessArticle

CloudFCN: Accurate and Robust Cloud Detection for Satellite Imagery with Deep Learning

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Mullard Space Science Laboratory, UCL, Holmbury Hill Rd, Dorking RH5 6NT, UK
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Cortexica Vision Systems Ltd., 30 Stamford Street, London SE1 9LQ, UK
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Hummingbird Technologies Ltd., 51 Hoxton Square, Hackney, London N1 6PB, UK
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Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2312; https://doi.org/10.3390/rs11192312
Received: 28 August 2019 / Revised: 24 September 2019 / Accepted: 30 September 2019 / Published: 6 October 2019
(This article belongs to the Section Remote Sensing Image Processing)
Cloud masking is of central importance to the Earth Observation community. This paper deals with the problem of detecting clouds in visible and multispectral imagery from high-resolution satellite cameras. Recently, Machine Learning has offered promising solutions to the problem of cloud masking, allowing for more flexibility than traditional thresholding techniques, which are restricted to instruments with the requisite spectral bands. However, few studies use multi-scale features (as in, a combination of pixel-level and spatial) whilst also offering compelling experimental evidence for real-world performance. Therefore, we introduce CloudFCN, based on a Fully Convolutional Network architecture, known as U-net, which has become a standard Deep Learning approach to image segmentation. It fuses the shallowest and deepest layers of the network, thus routing low-level visible content to its deepest layers. We offer an extensive range of experiments on this, including data from two high-resolution sensors—Carbonite-2 and Landsat 8—and several complementary tests. Owing to a variety of performance-enhancing design choices and training techniques, it exhibits state-of-the-art performance where comparable to other methods, high speed, and robustness to many different terrains and sensor types. View Full-Text
Keywords: clouds; deep learning; machine learning; computer vision; multispectral; optical clouds; deep learning; machine learning; computer vision; multispectral; optical
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MDPI and ACS Style

Francis, A.; Sidiropoulos, P.; Muller, J.-P. CloudFCN: Accurate and Robust Cloud Detection for Satellite Imagery with Deep Learning. Remote Sens. 2019, 11, 2312.

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