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There will be a significant impact on their performance, however when the training data are insufficient or when the label quality is low. Thus, to alleviate this problem, a semi-supervised cloud detection method, named the mean teacher cross-supervision cloud detection network (MTCSNet) is proposed. This method enforces both consistency and accuracy on two cloud detection student network branches, which are perturbed with different initializations, for the same input image. For each of the two student branches, the respective teacher branches, used to generate high-quality pseudo labels, are constructed using an exponential moving average method (EMA). A pseudo one-hot label, produced by one teacher network branch, supervises the other student network branch with the standard cross-entropy loss, and vice versa. To incorporate additional prior information into the model, the presented method uses near-infrared bands instead of red bands as model inputs and injects strong data augmentations on unlabeled images fed into the student model. This induces the model to learn richer representations and ensure consistency constraints on the predictions of the same unlabeled image across different batches. To attain a more refined equilibrium between the supervised and semi-supervised loss in the training process, the proposed cloud detection network learns the optimal weights based on homoscedastic uncertainty, thus effectively exploiting the advantages of semi-supervised tasks and elevating the overall performance. Experiments on the SPARCS and GF1-WHU public cloud detection datasets show that the proposed method outperforms several state-of-the-art semi-supervised algorithms when only a limited number of labeled samples are available.<\/jats:p>","DOI":"10.3390\/rs15082040","type":"journal-article","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T01:35:00Z","timestamp":1681349700000},"page":"2040","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["MTCSNet: Mean Teachers Cross-Supervision Network for Semi-Supervised Cloud Detection"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2847-7687","authenticated-orcid":false,"given":"Zongrui","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6756-0692","authenticated-orcid":false,"given":"Jun","family":"Pan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Zhuoer","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Mi","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0330-2683","authenticated-orcid":false,"given":"Likun","family":"Liu","sequence":"additional","affiliation":[{"name":"China Ship Development and Design Center, 268 Zhangzhidong Road, Wuhan 430064, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"D19105","DOI":"10.1029\/2003JD004457","article-title":"Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data","volume":"109","author":"Zhang","year":"2004","journal-title":"J. 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