{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T08:46:17Z","timestamp":1773909977921,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T00:00:00Z","timestamp":1675641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China 491","doi-asserted-by":"publisher","award":["51774250"],"award-info":[{"award-number":["51774250"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China 491","doi-asserted-by":"publisher","award":["2022NSFSC1113"],"award-info":[{"award-number":["2022NSFSC1113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China 491","doi-asserted-by":"publisher","award":["23QYCX0053"],"award-info":[{"award-number":["23QYCX0053"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Science and Technology Program","award":["51774250"],"award-info":[{"award-number":["51774250"]}]},{"name":"Sichuan Science and Technology Program","award":["2022NSFSC1113"],"award-info":[{"award-number":["2022NSFSC1113"]}]},{"name":"Sichuan Science and Technology Program","award":["23QYCX0053"],"award-info":[{"award-number":["23QYCX0053"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Clouds seriously limit the application of optical remote sensing images. In this paper, we remove clouds from satellite images using a novel method that considers ground surface reflections and cloud top reflections as a linear mixture of image elements from the perspective of image superposition. We use a two-step convolutional neural network to extract the transparency information of clouds and then recover the ground surface information of thin cloud regions. Given the poor balance of the generated samples, this paper also improves the binary Tversky loss function and applies it on multi-classification tasks. The model was validated on the simulated dataset and ALCD dataset, respectively. The results show that this model outperformed other control group experiments in cloud detection and removal. The model better locates the clouds in images with cloud matting, which is built based on cloud detection. In addition, the model successfully recovers the surface information of the thin cloud region when thick and thin clouds coexist, and it does not damage the original image\u2019s information.<\/jats:p>","DOI":"10.3390\/rs15040904","type":"journal-article","created":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T02:56:08Z","timestamp":1675738568000},"page":"904","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Cloud Removal from Satellite Images Using a Deep Learning Model with the Cloud-Matting Method"],"prefix":"10.3390","volume":"15","author":[{"given":"Deying","family":"Ma","sequence":"first","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"},{"name":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7168-7773","authenticated-orcid":false,"given":"Renzhe","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Dongsheng","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"}]},{"given":"Baikai","family":"Sui","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1016\/j.rse.2007.08.011","article-title":"The Availability of Cloud-Free Landsat ETM+ Data over the Conterminous United States and Globally","volume":"112","author":"Ju","year":"2008","journal-title":"Remote Sens. 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