{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:03:13Z","timestamp":1764784993607,"version":"build-2065373602"},"reference-count":68,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T00:00:00Z","timestamp":1666051200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Grant Agency of the Czech Technical University in Prague","award":["SGS22\/047\/OHK1\/1T\/11"],"award-info":[{"award-number":["SGS22\/047\/OHK1\/1T\/11"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In most parts of the electromagnetic spectrum, solar radiation cannot penetrate clouds. Therefore, cloud detection and masking are essential in image preprocessing for observing the Earth and analyzing its properties. Because clouds vary in size, shape, and structure, an accurate algorithm is required for removing them from the area of interest. This task is usually more challenging over bright surfaces such as exposed sunny deserts or snow than over water bodies or vegetated surfaces. The overarching goal of the current study is to explore and compare the performance of three Convolutional Neural Network architectures (U-Net, SegNet, and DeepLab) for detecting clouds in the VEN\u03bcS satellite images. To fulfil this goal, three VEN\u03bcS tiles in Israel were selected. The tiles represent different land-use and cover categories, including vegetated, urban, agricultural, and arid areas, as well as water bodies, with a special focus on bright desert surfaces. Additionally, the study examines the effect of various channel inputs, exploring possibilities of broader usage of these architectures for different data sources. It was found that among the tested architectures, U-Net performs the best in most settings. Its results on a simple RGB-based dataset indicate its potential value for any satellite system screening, at least in the visible spectrum. It is concluded that all of the tested architectures outperform the current VEN\u03bcS cloud-masking algorithm by lowering the false positive detection ratio by tens of percents, and should be considered an alternative by any user dealing with cloud-corrupted scenes.<\/jats:p>","DOI":"10.3390\/rs14205210","type":"journal-article","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:58:51Z","timestamp":1666141131000},"page":"5210","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Using Convolutional Neural Networks for Cloud Detection on VEN\u03bcS Images over Multiple Land-Cover Types"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2363-8002","authenticated-orcid":false,"given":"Ond\u0159ej","family":"Pe\u0161ek","sequence":"first","affiliation":[{"name":"Department of Geomatics, Faculty of Civil Engineering, Czech Technical University in Prague, 166 29 Prague, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7577-4268","authenticated-orcid":false,"given":"Michal","family":"Segal-Rozenhaimer","sequence":"additional","affiliation":[{"name":"Department of Geophysics, Porter School of the Environment and Earth Sciences, Tel-Aviv University, Ramat-Aviv 699 78, Israel"},{"name":"Bay Area Environmental Research Institute, NASA Ames Research Center, Moffett Field, Mountain View, CA 940 35, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8065-9793","authenticated-orcid":false,"given":"Arnon","family":"Karnieli","sequence":"additional","affiliation":[{"name":"The Remote Sensing Laboratory, French Associates Institute for Agriculture and Biotechnology of Drylands, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion 8499000, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3826","DOI":"10.1109\/TGRS.2012.2227333","article-title":"Spatial and Temporal Distribution of Clouds Observed by MODIS Onboard the Terra and Aqua Satellites","volume":"51","author":"King","year":"2013","journal-title":"IEEE Trans. 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