{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T15:34:48Z","timestamp":1777995288401,"version":"3.51.4"},"reference-count":34,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,9]],"date-time":"2018-05-09T00:00:00Z","timestamp":1525824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Nation key R&amp;D Program of China","award":["2016YFC0803100"],"award-info":[{"award-number":["2016YFC0803100"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41101452"],"award-info":[{"award-number":["41101452"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012470","name":"Doctoral Program Foundation of Institutions of Higher Education of China","doi-asserted-by":"publisher","award":["20112121120003"],"award-info":[{"award-number":["20112121120003"]}],"id":[{"id":"10.13039\/501100012470","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>In high-resolution image data, multilevel cloud detection is a key task for remote sensing data processing. Generally, it is difficult to obtain high accuracy for multilevel cloud detection when using satellite imagery which only contains visible and near-infrared spectral bands. So, multilevel cloud detection for high-resolution remote sensing imagery is challenging. In this paper, a new multilevel cloud detection technique is proposed based on the multiple convolutional neural networks for high-resolution remote sensing imagery. In order to avoid input the entire image into the network for cloud detection, the adaptive simple linear iterative clustering (A-SCLI) algorithm was applied to the segmentation of the satellite image to obtain good-quality superpixels. After that, a new multiple convolutional neural networks (MCNNs) architecture is designed to extract multiscale features from each superpixel, and the superpixels are marked as thin cloud, thick cloud, cloud shadow, and non-cloud. The results suggest that the proposed method can detect multilevel clouds and obtain a high accuracy for high-resolution remote sensing imagery.<\/jats:p>","DOI":"10.3390\/ijgi7050181","type":"journal-article","created":{"date-parts":[[2018,5,10]],"date-time":"2018-05-10T03:48:27Z","timestamp":1525924107000},"page":"181","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["Multilevel Cloud Detection for High-Resolution Remote Sensing Imagery Using Multiple Convolutional Neural Networks"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3407-7845","authenticated-orcid":false,"given":"Yang","family":"Chen","sequence":"first","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"},{"name":"Chinese Academy of Surveying and Mapping, Beijing 100830, China"}]},{"given":"Rongshuang","family":"Fan","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping, Beijing 100830, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1022-3999","authenticated-orcid":false,"given":"Muhammad","family":"Bilal","sequence":"additional","affiliation":[{"name":"School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Xiucheng","family":"Yang","sequence":"additional","affiliation":[{"name":"ICube Laboratory, University of Strasbourg, 67000 Strasbourg, France"}]},{"given":"Jingxue","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"Mining Engineering Institute, Heilongjiang University of Science and Technology, Harbin 150027, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zortea, M., De Martino, M., and Serpico, S. 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