{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T05:24:43Z","timestamp":1771565083109,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T00:00:00Z","timestamp":1672790400000},"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":["41871305"],"award-info":[{"award-number":["41871305"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["GLAB2022ZR06"],"award-info":[{"award-number":["GLAB2022ZR06"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42122025"],"award-info":[{"award-number":["42122025"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education","award":["41871305"],"award-info":[{"award-number":["41871305"]}]},{"name":"Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education","award":["GLAB2022ZR06"],"award-info":[{"award-number":["GLAB2022ZR06"]}]},{"name":"Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education","award":["42122025"],"award-info":[{"award-number":["42122025"]}]},{"name":"National Science Foundation for Outstanding Young Scholars","award":["41871305"],"award-info":[{"award-number":["41871305"]}]},{"name":"National Science Foundation for Outstanding Young Scholars","award":["GLAB2022ZR06"],"award-info":[{"award-number":["GLAB2022ZR06"]}]},{"name":"National Science Foundation for Outstanding Young Scholars","award":["42122025"],"award-info":[{"award-number":["42122025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-resolution DEMs can provide accurate geographic information and can be widely used in hydrological analysis, path planning, and urban design. As the main complementary means of producing high-resolution DEMs, the DEM super-resolution (SR) method based on deep learning has reached a bottleneck. The reason for this phenomenon is that the DEM super-resolution method based on deep learning lacks a part of the global information it requires. Specifically, the multilevel aggregation process of deep learning has difficulty sufficiently capturing the low-level features with dependencies, which leads to a lack of global relationships with high-level information. To address this problem, we propose a global-information-constrained deep learning network for DEM SR (GISR). Specifically, our proposed GISR method consists of a global information supplement module and a local feature generation module. The former uses the Kriging method to supplement global information, considering the spatial autocorrelation rule. The latter includes a residual module and the PixelShuffle module, which is used to restore the detailed features of the terrain. Compared with the bicubic, Kriging, SRCNN, SRResNet, and TfaSR methods, the experimental results of our method show a better ability to retain terrain features, and the generation effect is more consistent with the ground truth DEM. Meanwhile, compared with the deep learning method, the RMSE of our results is improved by 20.5% to 68.8%.<\/jats:p>","DOI":"10.3390\/rs15020305","type":"journal-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T02:00:57Z","timestamp":1672884057000},"page":"305","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["A Global-Information-Constrained Deep Learning Network for Digital Elevation Model Super-Resolution"],"prefix":"10.3390","volume":"15","author":[{"given":"Xiaoyi","family":"Han","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Xiaochuan","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Houpu","family":"Li","sequence":"additional","affiliation":[{"name":"Control Engineering Laboratory, Naval University of Engineering, Wuhan 430030, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6373-3162","authenticated-orcid":false,"given":"Zhanlong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China"},{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"},{"name":"National Engineering Research Center of Geographic Information System, Wuhan 430078, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Passalacqua, P., Tarolli, P., and Foufoula-Georgiou, E. 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