{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:48:39Z","timestamp":1778860119889,"version":"3.51.4"},"reference-count":39,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,10]],"date-time":"2019-01-10T00:00:00Z","timestamp":1547078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MoST 107-2611-M-006-002"],"award-info":[{"award-number":["MoST 107-2611-M-006-002"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MoST 107-2622-8-006-008-TA"],"award-info":[{"award-number":["MoST 107-2622-8-006-008-TA"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Soil and Water Conservation Bureau, Council of Agriculture, Taiwan","award":["SWCB-107-097"],"award-info":[{"award-number":["SWCB-107-097"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Detecting changes in land use and land cover (LULC) from space has long been the main goal of satellite remote sensing (RS), yet the existing and available algorithms for cloud classification are not reliable enough to attain this goal in an automated fashion. Clouds are very strong optical signals that dominate the results of change detection if they are not removed completely from imagery. As various architectures of deep learning (DL) have been proposed and advanced quickly, their potential in perceptual tasks has been widely accepted and successfully applied to many fields. A comprehensive survey of DL in RS has been reviewed, and the RS community has been suggested to be leading researchers in DL. Based on deep residual learning, semantic image segmentation, and the concept of atrous convolution, we propose a new DL architecture, named CloudNet, with an enhanced capability of feature extraction for classifying cloud and haze from Sentinel-2 imagery, with the intention of supporting automatic change detection in LULC. To ensure the quality of the training dataset, scene classification maps of Taiwan processed by Sen2cor were visually examined and edited, resulting in a total of 12,769 sub-images with a standard size of 224 \u00d7 224 pixels, cut from the Sen2cor-corrected images and compiled in a trainset. The data augmentation technique enabled CloudNet to have stable cirrus identification capability without extensive training data. Compared to the traditional method and other DL methods, CloudNet had higher accuracy in cloud and haze classification, as well as better performance in cirrus cloud recognition. CloudNet will be incorporated into the Open Access Satellite Image Service to facilitate change detection by using Sentinel-2 imagery on a regular and automatic basis.<\/jats:p>","DOI":"10.3390\/rs11020119","type":"journal-article","created":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T04:10:16Z","timestamp":1547179816000},"page":"119","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Clouds Classification from Sentinel-2 Imagery with Deep Residual Learning and Semantic Image Segmentation"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5074-7124","authenticated-orcid":false,"given":"Cheng-Chien","family":"Liu","sequence":"first","affiliation":[{"name":"Global Earth Observation and Data Analysis Center, National Cheng Kung University, Tainan City 70101, Taiwan"},{"name":"Department of Earth Sciences, National Cheng Kung University, Tainan City 70101, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Cheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City 70101, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pei-Yin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City 70101, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chien-Chih","family":"Lai","sequence":"additional","affiliation":[{"name":"Global Earth Observation and Data Analysis Center, National Cheng Kung University, Tainan City 70101, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi-Hsin","family":"Chen","sequence":"additional","affiliation":[{"name":"Global Earth Observation and Data Analysis Center, National Cheng Kung University, Tainan City 70101, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ji-Hong","family":"Cheng","sequence":"additional","affiliation":[{"name":"Global Earth Observation and Data Analysis Center, National Cheng Kung University, Tainan City 70101, Taiwan"},{"name":"Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City 70101, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming-Hsun","family":"Ko","sequence":"additional","affiliation":[{"name":"Global Earth Observation and Data Analysis Center, National Cheng Kung University, Tainan City 70101, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Review article digital change detection techniques using remotely-sensed data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. 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