Loading…

A Transformer-Unet Generative Adversarial Network for the Super-Resolution Reconstruction of DEMs

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 sel...

Full description

Saved in:
Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-10, Vol.16 (19), p.3676
Main Authors: Zheng, Xin, Xu, Zhaoqi, Yin, Qian, Bao, Zelun, Chen, Zhirui, Wang, Sizhu
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites cdi_FETCH-LOGICAL-c289t-bed4631c2b8d1296162612aeb2950e89d95f62e4c3c1a0f892832e8b4238b90f3
container_end_page
container_issue 19
container_start_page 3676
container_title Remote sensing (Basel, Switzerland)
container_volume 16
creator Zheng, Xin
Xu, Zhaoqi
Yin, Qian
Bao, Zelun
Chen, Zhirui
Wang, Sizhu
description 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.
doi_str_mv 10.3390/rs16193676
format article
fullrecord <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_b24905f9ddee40fca4ca8022d58c2cb1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A814409552</galeid><doaj_id>oai_doaj_org_article_b24905f9ddee40fca4ca8022d58c2cb1</doaj_id><sourcerecordid>A814409552</sourcerecordid><originalsourceid>FETCH-LOGICAL-c289t-bed4631c2b8d1296162612aeb2950e89d95f62e4c3c1a0f892832e8b4238b90f3</originalsourceid><addsrcrecordid>eNpNUV1rFTEQXcSCpfalv2DBN2FrMsmmyeOl1rZQLfTjOWSTSc313s11kq347429os48zAfnHA4zXXfC2akQhn2gwhU3Qp2pV90hsDMYJBh4_V__pjsuZc1aCMENk4edW_UP5OYSM22RhscZa3-JM5Kr6Rn7VXhGKo6S2_RfsP7I9K1v0L5-xf5-2TXGHZa8WWrKc3-HPs-l0uJfxhz7jxefy9vuILpNweM_9ah7_HTxcH413NxeXp-vbgYP2tRhwiCV4B4mHTgYxRUoDg4nMCNDbYIZowKUXnjuWNQGtADUkwShJ8OiOOqu97ohu7XdUdo6-mmzS_ZlkenJOqrJb9BOIA0bowkBUbLonfROM4Awag9-4k3r3V5rR_n7gqXadV5obvat4Fyp0XBQDXW6Rz25JprmmCs53zLgNrVTYExtv9JcSmbGERrh_Z7gKZdCGP_a5Mz-fqH990LxC1zCjV4</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3116659126</pqid></control><display><type>article</type><title>A Transformer-Unet Generative Adversarial Network for the Super-Resolution Reconstruction of DEMs</title><source>Publicly Available Content Database</source><creator>Zheng, Xin ; Xu, Zhaoqi ; Yin, Qian ; Bao, Zelun ; Chen, Zhirui ; Wang, Sizhu</creator><creatorcontrib>Zheng, Xin ; Xu, Zhaoqi ; Yin, Qian ; Bao, Zelun ; Chen, Zhirui ; Wang, Sizhu</creatorcontrib><description>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.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><identifier>DOI: 10.3390/rs16193676</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Artificial intelligence ; Comparative analysis ; Deep learning ; DEM ; Digital Elevation Models ; Discriminators ; Electronic data processing ; Elevation ; Environmental science ; Feedback ; GAN ; Generative adversarial networks ; Geology ; Machine learning ; Methods ; Multilayers ; Neural networks ; Reconstruction ; Self-similarity ; super-resolution ; Technology application ; Topography ; transformer ; Transformers ; U-Net</subject><ispartof>Remote sensing (Basel, Switzerland), 2024-10, Vol.16 (19), p.3676</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c289t-bed4631c2b8d1296162612aeb2950e89d95f62e4c3c1a0f892832e8b4238b90f3</cites><orcidid>0000-0002-0354-5490 ; 0000-0001-7585-4156</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3116659126/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3116659126?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590,75126</link.rule.ids></links><search><creatorcontrib>Zheng, Xin</creatorcontrib><creatorcontrib>Xu, Zhaoqi</creatorcontrib><creatorcontrib>Yin, Qian</creatorcontrib><creatorcontrib>Bao, Zelun</creatorcontrib><creatorcontrib>Chen, Zhirui</creatorcontrib><creatorcontrib>Wang, Sizhu</creatorcontrib><title>A Transformer-Unet Generative Adversarial Network for the Super-Resolution Reconstruction of DEMs</title><title>Remote sensing (Basel, Switzerland)</title><description>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.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Comparative analysis</subject><subject>Deep learning</subject><subject>DEM</subject><subject>Digital Elevation Models</subject><subject>Discriminators</subject><subject>Electronic data processing</subject><subject>Elevation</subject><subject>Environmental science</subject><subject>Feedback</subject><subject>GAN</subject><subject>Generative adversarial networks</subject><subject>Geology</subject><subject>Machine learning</subject><subject>Methods</subject><subject>Multilayers</subject><subject>Neural networks</subject><subject>Reconstruction</subject><subject>Self-similarity</subject><subject>super-resolution</subject><subject>Technology application</subject><subject>Topography</subject><subject>transformer</subject><subject>Transformers</subject><subject>U-Net</subject><issn>2072-4292</issn><issn>2072-4292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNUV1rFTEQXcSCpfalv2DBN2FrMsmmyeOl1rZQLfTjOWSTSc313s11kq347429os48zAfnHA4zXXfC2akQhn2gwhU3Qp2pV90hsDMYJBh4_V__pjsuZc1aCMENk4edW_UP5OYSM22RhscZa3-JM5Kr6Rn7VXhGKo6S2_RfsP7I9K1v0L5-xf5-2TXGHZa8WWrKc3-HPs-l0uJfxhz7jxefy9vuILpNweM_9ah7_HTxcH413NxeXp-vbgYP2tRhwiCV4B4mHTgYxRUoDg4nMCNDbYIZowKUXnjuWNQGtADUkwShJ8OiOOqu97ohu7XdUdo6-mmzS_ZlkenJOqrJb9BOIA0bowkBUbLonfROM4Awag9-4k3r3V5rR_n7gqXadV5obvat4Fyp0XBQDXW6Rz25JprmmCs53zLgNrVTYExtv9JcSmbGERrh_Z7gKZdCGP_a5Mz-fqH990LxC1zCjV4</recordid><startdate>20241001</startdate><enddate>20241001</enddate><creator>Zheng, Xin</creator><creator>Xu, Zhaoqi</creator><creator>Yin, Qian</creator><creator>Bao, Zelun</creator><creator>Chen, Zhirui</creator><creator>Wang, Sizhu</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PCBAR</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-0354-5490</orcidid><orcidid>https://orcid.org/0000-0001-7585-4156</orcidid></search><sort><creationdate>20241001</creationdate><title>A Transformer-Unet Generative Adversarial Network for the Super-Resolution Reconstruction of DEMs</title><author>Zheng, Xin ; Xu, Zhaoqi ; Yin, Qian ; Bao, Zelun ; Chen, Zhirui ; Wang, Sizhu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c289t-bed4631c2b8d1296162612aeb2950e89d95f62e4c3c1a0f892832e8b4238b90f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Comparative analysis</topic><topic>Deep learning</topic><topic>DEM</topic><topic>Digital Elevation Models</topic><topic>Discriminators</topic><topic>Electronic data processing</topic><topic>Elevation</topic><topic>Environmental science</topic><topic>Feedback</topic><topic>GAN</topic><topic>Generative adversarial networks</topic><topic>Geology</topic><topic>Machine learning</topic><topic>Methods</topic><topic>Multilayers</topic><topic>Neural networks</topic><topic>Reconstruction</topic><topic>Self-similarity</topic><topic>super-resolution</topic><topic>Technology application</topic><topic>Topography</topic><topic>transformer</topic><topic>Transformers</topic><topic>U-Net</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zheng, Xin</creatorcontrib><creatorcontrib>Xu, Zhaoqi</creatorcontrib><creatorcontrib>Yin, Qian</creatorcontrib><creatorcontrib>Bao, Zelun</creatorcontrib><creatorcontrib>Chen, Zhirui</creatorcontrib><creatorcontrib>Wang, Sizhu</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric &amp; Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ANTE: Abstracts in New Technology &amp; Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Engineering Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Earth, Atmospheric &amp; Aquatic Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Directory of Open Access Journals</collection><jtitle>Remote sensing (Basel, Switzerland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zheng, Xin</au><au>Xu, Zhaoqi</au><au>Yin, Qian</au><au>Bao, Zelun</au><au>Chen, Zhirui</au><au>Wang, Sizhu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Transformer-Unet Generative Adversarial Network for the Super-Resolution Reconstruction of DEMs</atitle><jtitle>Remote sensing (Basel, Switzerland)</jtitle><date>2024-10-01</date><risdate>2024</risdate><volume>16</volume><issue>19</issue><spage>3676</spage><pages>3676-</pages><issn>2072-4292</issn><eissn>2072-4292</eissn><abstract>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.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/rs16193676</doi><orcidid>https://orcid.org/0000-0002-0354-5490</orcidid><orcidid>https://orcid.org/0000-0001-7585-4156</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2072-4292
ispartof Remote sensing (Basel, Switzerland), 2024-10, Vol.16 (19), p.3676
issn 2072-4292
2072-4292
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_b24905f9ddee40fca4ca8022d58c2cb1
source Publicly Available Content Database
subjects Algorithms
Artificial intelligence
Comparative analysis
Deep learning
DEM
Digital Elevation Models
Discriminators
Electronic data processing
Elevation
Environmental science
Feedback
GAN
Generative adversarial networks
Geology
Machine learning
Methods
Multilayers
Neural networks
Reconstruction
Self-similarity
super-resolution
Technology application
Topography
transformer
Transformers
U-Net
title A Transformer-Unet Generative Adversarial Network for the Super-Resolution Reconstruction of DEMs
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T16%3A37%3A31IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Transformer-Unet%20Generative%20Adversarial%20Network%20for%20the%20Super-Resolution%20Reconstruction%20of%20DEMs&rft.jtitle=Remote%20sensing%20(Basel,%20Switzerland)&rft.au=Zheng,%20Xin&rft.date=2024-10-01&rft.volume=16&rft.issue=19&rft.spage=3676&rft.pages=3676-&rft.issn=2072-4292&rft.eissn=2072-4292&rft_id=info:doi/10.3390/rs16193676&rft_dat=%3Cgale_doaj_%3EA814409552%3C/gale_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c289t-bed4631c2b8d1296162612aeb2950e89d95f62e4c3c1a0f892832e8b4238b90f3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3116659126&rft_id=info:pmid/&rft_galeid=A814409552&rfr_iscdi=true