A Multi-Path Feature Extraction and Transformer Feature Enhancement DEM Super-Resolution Reconstruction Network
In recent years, great progress has been made in the field of super-resolution (SR) reconstruction based on deep learning techniques. Although image SR techniques show strong potential in image reconstruction, the effective application of these techniques to SR reconstruction of digital elevation mo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/10/1737 |
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| author | Mingqiang Guo Feng Xiong Ying Huang Zhizheng Zhang Jiaming Zhang |
| author_facet | Mingqiang Guo Feng Xiong Ying Huang Zhizheng Zhang Jiaming Zhang |
| author_sort | Mingqiang Guo |
| collection | DOAJ |
| description | In recent years, great progress has been made in the field of super-resolution (SR) reconstruction based on deep learning techniques. Although image SR techniques show strong potential in image reconstruction, the effective application of these techniques to SR reconstruction of digital elevation models (DEMs) remains an important research challenge. The complexity and diversity of DEMs limits existing methods to capture subtle changes and features of the terrain, thus affecting the quality of reconstruction. To solve this problem, a DEM SR reconstruction network based on multi-path feature extraction and transformer feature enhancement is proposed in this paper. The network structure has three parts: feature extraction, image reconstruction, and feature enhancement. The feature extraction component consists of three feature extraction blocks, and each feature extraction block contains multiple multi-path feature residuals to enhance the interaction between spatial information and semantic information, so as to fully extract image features. In addition, the transformer feature enhancement module uses an encoder and decoder based design, leveraging the correlation between low- and high-dimensional features to further improve network performance. Through repeated testing and improvement, the model shows excellent performance in high-resolution DEM image reconstruction, and can generate more accurate DEMs. In terms of elevation and slope evaluation indexes, the model was 3.41% and 1.11% better compared with the existing reconstruction methods, which promotes the application of SR reconstruction technology in terrain data. |
| format | Article |
| id | doaj-art-17b47e4399a9477494783e2df1e1247d |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-17b47e4399a9477494783e2df1e1247d2025-08-20T03:12:05ZengMDPI AGRemote Sensing2072-42922025-05-011710173710.3390/rs17101737A Multi-Path Feature Extraction and Transformer Feature Enhancement DEM Super-Resolution Reconstruction NetworkMingqiang Guo0Feng Xiong1Ying Huang2Zhizheng Zhang3Jiaming Zhang4School of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaWuhan Zondy Cyber Technology Co., Ltd., Wuhan 430074, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430074, ChinaCollege of Engineering, Boston University, Boston, MA 02215, USAIn recent years, great progress has been made in the field of super-resolution (SR) reconstruction based on deep learning techniques. Although image SR techniques show strong potential in image reconstruction, the effective application of these techniques to SR reconstruction of digital elevation models (DEMs) remains an important research challenge. The complexity and diversity of DEMs limits existing methods to capture subtle changes and features of the terrain, thus affecting the quality of reconstruction. To solve this problem, a DEM SR reconstruction network based on multi-path feature extraction and transformer feature enhancement is proposed in this paper. The network structure has three parts: feature extraction, image reconstruction, and feature enhancement. The feature extraction component consists of three feature extraction blocks, and each feature extraction block contains multiple multi-path feature residuals to enhance the interaction between spatial information and semantic information, so as to fully extract image features. In addition, the transformer feature enhancement module uses an encoder and decoder based design, leveraging the correlation between low- and high-dimensional features to further improve network performance. Through repeated testing and improvement, the model shows excellent performance in high-resolution DEM image reconstruction, and can generate more accurate DEMs. In terms of elevation and slope evaluation indexes, the model was 3.41% and 1.11% better compared with the existing reconstruction methods, which promotes the application of SR reconstruction technology in terrain data.https://www.mdpi.com/2072-4292/17/10/1737digital elevation modelsuper-resolution reconstructionmulti-path feature extractiontransformer feature enhancement |
| spellingShingle | Mingqiang Guo Feng Xiong Ying Huang Zhizheng Zhang Jiaming Zhang A Multi-Path Feature Extraction and Transformer Feature Enhancement DEM Super-Resolution Reconstruction Network Remote Sensing digital elevation model super-resolution reconstruction multi-path feature extraction transformer feature enhancement |
| title | A Multi-Path Feature Extraction and Transformer Feature Enhancement DEM Super-Resolution Reconstruction Network |
| title_full | A Multi-Path Feature Extraction and Transformer Feature Enhancement DEM Super-Resolution Reconstruction Network |
| title_fullStr | A Multi-Path Feature Extraction and Transformer Feature Enhancement DEM Super-Resolution Reconstruction Network |
| title_full_unstemmed | A Multi-Path Feature Extraction and Transformer Feature Enhancement DEM Super-Resolution Reconstruction Network |
| title_short | A Multi-Path Feature Extraction and Transformer Feature Enhancement DEM Super-Resolution Reconstruction Network |
| title_sort | multi path feature extraction and transformer feature enhancement dem super resolution reconstruction network |
| topic | digital elevation model super-resolution reconstruction multi-path feature extraction transformer feature enhancement |
| url | https://www.mdpi.com/2072-4292/17/10/1737 |
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