@inproceedings{de0e243cf1da4f7c925bb60a6b2c3d3e,
title = "FAST ACCURATE SUPERVISED CLOUD ANNOTATION",
abstract = "Using optical satellite images requires detecting accurately all clouds in any image. For many applications, automatic cloud detection methods are not accurate enough. We describe here a fast machine learning based annotation system and demonstrate on Sentinel-2 images its efficacy to reach in four clicks or less a more than 95\% accurate cloud detector. To obtain these statistics, we constructed an eclectic database of partially cloudy images and its ground truth, and evaluated its accuracy to be larger than 98\%. We then show that our fast supervised annotation is far more accurate than recent sophisticated cloud detectors. {\textcopyright} 2021 IEEE",
keywords = "classification, cloud detection, Optical satellite images, segmentation, supervised learning",
author = "C. Williams and T. Dagobert and \{de Franchis\}, C. and J.-M. Morel and C. Hessel",
year = "2021",
doi = "10.1109/IGARSS47720.2021.9554213",
language = "English",
isbn = "978-1-6654-4762-1",
publisher = "IEEE",
pages = "3237--3240",
booktitle = "IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium",
address = "United States",
note = "2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2021) ; Conference date: 11-07-2021 Through 16-07-2021",
}