{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T04:09:48Z","timestamp":1770523788671,"version":"3.49.0"},"reference-count":32,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"STI2030-Major Projects","award":["2022ZD0205000"],"award-info":[{"award-number":["2022ZD0205000"]}]},{"name":"STI2030-Major Projects","award":["U2031136"],"award-info":[{"award-number":["U2031136"]}]},{"name":"Joint Research Fund in Astronomy","award":["2022ZD0205000"],"award-info":[{"award-number":["2022ZD0205000"]}]},{"name":"Joint Research Fund in Astronomy","award":["U2031136"],"award-info":[{"award-number":["U2031136"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A new model called the Transformer-Unet Generative Adversarial Network (TUGAN) is proposed for super-resolution reconstruction of digital elevation models (DEMs). Digital elevation models are used in many fields, including environmental science, geology and agriculture. The proposed model uses a self-similarity Transformer (SSTrans) as the generator and U-Net as the discriminator. SSTrans, a model that we previously proposed, can yield good reconstruction results in structurally complex areas but has little advantage when the surface is simple and smooth because too many additional details have been added to the data. To resolve this issue, we propose the novel TUGAN model, where U-Net is capable of multilayer jump connections, which enables the discriminator to consider both global and local information when making judgments. The experiments show that TUGAN achieves state-of-the-art results for all types of terrain details.<\/jats:p>","DOI":"10.3390\/rs16193676","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T11:08:47Z","timestamp":1727780927000},"page":"3676","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Transformer-Unet Generative Adversarial Network for the Super-Resolution Reconstruction of DEMs"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7585-4156","authenticated-orcid":false,"given":"Xin","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China"}]},{"given":"Zhaoqi","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0354-5490","authenticated-orcid":false,"given":"Qian","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China"}]},{"given":"Zelun","family":"Bao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China"}]},{"given":"Zhirui","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China"}]},{"given":"Sizhu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,1]]},"reference":[{"key":"ref_1","first-page":"S855","article-title":"Modelling urban floods at submetre resolution: Challenges or opportunities for flood risk management?","volume":"11","author":"Bates","year":"2018","journal-title":"J. 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