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Article

KappaMask: AI-Based Cloudmask Processor for Sentinel-2

1
KappaZeta Ltd., 51007 Tartu, Estonia
2
Institute of Computer Science, University of Tartu Estonia, 51009 Tartu, Estonia
3
Tartu Observatory, University of Tartu, 61602 Tõravere, Estonia
4
European Space Agency, ESA-ESRIN, Largo Galileo Galilei, 1, 00044 Frascati, RM, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Claudio Piciarelli, Hyungtae Lee and Sungmin Eum
Remote Sens. 2021, 13(20), 4100; https://doi.org/10.3390/rs13204100
Received: 19 August 2021 / Revised: 3 October 2021 / Accepted: 8 October 2021 / Published: 13 October 2021
(This article belongs to the Special Issue Computer Vision and Deep Learning for Remote Sensing Applications)
The Copernicus Sentinel-2 mission operated by the European Space Agency (ESA) provides comprehensive and continuous multi-spectral observations of all the Earth’s land surface since mid-2015. Clouds and cloud shadows significantly decrease the usability of optical satellite data, especially in agricultural applications; therefore, an accurate and reliable cloud mask is mandatory for effective EO optical data exploitation. During the last few years, image segmentation techniques have developed rapidly with the exploitation of neural network capabilities. With this perspective, the KappaMask processor using U-Net architecture was developed with the ability to generate a classification mask over northern latitudes into the following classes: clear, cloud shadow, semi-transparent cloud (thin clouds), cloud and invalid. For training, a Sentinel-2 dataset covering the Northern European terrestrial area was labelled. KappaMask provides a 10 m classification mask for Sentinel-2 Level-2A (L2A) and Level-1C (L1C) products. The total dice coefficient on the test dataset, which was not seen by the model at any stage, was 80% for KappaMask L2A and 76% for KappaMask L1C for clear, cloud shadow, semi-transparent and cloud classes. A comparison with rule-based cloud mask methods was then performed on the same test dataset, where Sen2Cor reached 59% dice coefficient for clear, cloud shadow, semi-transparent and cloud classes, Fmask reached 61% for clear, cloud shadow and cloud classes and Maja reached 51% for clear and cloud classes. The closest machine learning open-source cloud classification mask, S2cloudless, had a 63% dice coefficient providing only cloud and clear classes, while KappaMask L2A, with a more complex classification schema, outperformed S2cloudless by 17%. View Full-Text
Keywords: convolutional neural network; cloud mask; Sentinel-2; KappaMask; active learning; image segmentation; remote sensing convolutional neural network; cloud mask; Sentinel-2; KappaMask; active learning; image segmentation; remote sensing
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MDPI and ACS Style

Domnich, M.; Sünter, I.; Trofimov, H.; Wold, O.; Harun, F.; Kostiukhin, A.; Järveoja, M.; Veske, M.; Tamm, T.; Voormansik, K.; Olesk, A.; Boccia, V.; Longepe, N.; Cadau, E.G. KappaMask: AI-Based Cloudmask Processor for Sentinel-2. Remote Sens. 2021, 13, 4100. https://doi.org/10.3390/rs13204100

AMA Style

Domnich M, Sünter I, Trofimov H, Wold O, Harun F, Kostiukhin A, Järveoja M, Veske M, Tamm T, Voormansik K, Olesk A, Boccia V, Longepe N, Cadau EG. KappaMask: AI-Based Cloudmask Processor for Sentinel-2. Remote Sensing. 2021; 13(20):4100. https://doi.org/10.3390/rs13204100

Chicago/Turabian Style

Domnich, Marharyta, Indrek Sünter, Heido Trofimov, Olga Wold, Fariha Harun, Anton Kostiukhin, Mihkel Järveoja, Mihkel Veske, Tanel Tamm, Kaupo Voormansik, Aire Olesk, Valentina Boccia, Nicolas Longepe, and Enrico Giuseppe Cadau. 2021. "KappaMask: AI-Based Cloudmask Processor for Sentinel-2" Remote Sensing 13, no. 20: 4100. https://doi.org/10.3390/rs13204100

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