{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T08:00:40Z","timestamp":1777363240502,"version":"3.51.4"},"reference-count":27,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:00:00Z","timestamp":1659484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory of Optical Calibration and Characterization KLOCC","award":["2108085MF232"],"award-info":[{"award-number":["2108085MF232"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Cloud detection is one of the critical tasks in remote sensing image preprocessing. Remote sensing images usually contain multi-dimensional information, which is not utilized entirely in existing deep learning methods. This paper proposes a novel cloud detection algorithm based on multi-scale input and dual-channel attention mechanisms. Firstly, we remodeled the original data to a multi-scale layout in terms of channels and bands. Then, we introduced the dual-channel attention mechanism into the existing semantic segmentation network, to focus on both band information and angle information based on the reconstructed multi-scale data. Finally, a multi-scale fusion strategy was introduced to combine band information and angle information simultaneously. Overall, in the experiments undertaken in this paper, the proposed method achieved a pixel accuracy of 92.66% and a category pixel accuracy of 92.51%. For cloud detection, the proposed method achieved a recall of 97.76% and an F1 of 95.06%. The intersection over union (IoU) of the proposed method was 89.63%. Both in terms of quantitative results and visual effects, the deep learning model we propose is superior to the existing semantic segmentation methods.<\/jats:p>","DOI":"10.3390\/rs14153710","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T23:33:01Z","timestamp":1659569581000},"page":"3710","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Cloud Detection of Remote Sensing Image Based on Multi-Scale Data and Dual-Channel Attention Mechanism"],"prefix":"10.3390","volume":"14","author":[{"given":"Qing","family":"Yan","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hu","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingjing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaobing","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Optical Calibration and Characterization, Chinese Academy of Sciences, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Xiong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Optical Calibration and Characterization, Chinese Academy of Sciences, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingmin","family":"Zou","sequence":"additional","affiliation":[{"name":"Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Xia","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lina","family":"Xun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4435","DOI":"10.1175\/2011JCLI3857.1","article-title":"Examination of POLDER\/PARASOL and MODIS\/Aqua Cloud Fractions and Properties Representativeness","volume":"24","author":"Zeng","year":"2011","journal-title":"J. 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