{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:37:52Z","timestamp":1777653472941,"version":"3.51.4"},"reference-count":62,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2019,8,2]],"date-time":"2019-08-02T00:00:00Z","timestamp":1564704000000},"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","doi-asserted-by":"publisher","award":["41725006"],"award-info":[{"award-number":["41725006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images. Spatial attraction, geostatistics, and using prior geographic information are conventional approaches used to derive fine-scale land cover maps. As the convolutional neural network (CNN) has been shown to be effective in capturing the spatial characteristics of geographic objects and extrapolating calibrated methods to other study areas, it may be a useful approach to overcome limitations of current SRM methods. In this paper, a new SRM method based on the CNN (      SRM   CNN      ) is proposed and tested. Specifically, an encoder-decoder CNN is used to model the nonlinear relationship between coarse remote sensing images and fine-scale land cover maps. Two real-image experiments were conducted to analyze the effectiveness of the proposed method. The results demonstrate that the overall accuracy of the proposed       SRM   CNN       method was 3% to 5% higher than that of two existing SRM methods. Moreover, the proposed       SRM   CNN       method was validated by visualizing output features and analyzing the performance of different geographic objects.<\/jats:p>","DOI":"10.3390\/rs11151815","type":"journal-article","created":{"date-parts":[[2019,8,2]],"date-time":"2019-08-02T11:58:16Z","timestamp":1564747096000},"page":"1815","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2721-7456","authenticated-orcid":false,"given":"Yuanxin","family":"Jia","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yong","family":"Ge","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yuehong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China"}]},{"given":"Sanping","family":"Li","sequence":"additional","affiliation":[{"name":"DELLEMC CTO TRIGr, Beijing 100084, China"}]},{"given":"Gerard B.M.","family":"Heuvelink","sequence":"additional","affiliation":[{"name":"Soil Geography and Landscape Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0685-4897","authenticated-orcid":false,"given":"Feng","family":"Ling","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.rse.2014.03.034","article-title":"Incorporating spatial information in spectral unmixing: A review","volume":"149","author":"Shi","year":"2014","journal-title":"Remote Sens. Entviron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.rse.2018.04.030","article-title":"The mixed pixel effect in land surface phenology: A simulation study","volume":"211","author":"Chen","year":"2018","journal-title":"Remote Sens. 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