{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:08:15Z","timestamp":1773245295304,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T00:00:00Z","timestamp":1676332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Nature Science Foundation of China","award":["41671380"],"award-info":[{"award-number":["41671380"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The scale of digital elevation models (DEMs) is vital for terrain analysis, surface simulation, and other geographic applications. Compared to traditional super-resolution (SR) methods, deep convolutional neural networks (CNNs) have shown great success in DEM SR. However, in terms of these CNN-based SR methods, the features extracted by the stackable residual modules cannot be fully utilized as the depth of the network increases. Therefore, our study proposes an enhanced residual feature fusion network (ERFFN) for DEM SR. The designed residual fusion module groups four residual modules to make better use of the local residual features. Meanwhile, the residual structure is refined by inserting a lightweight enhanced spatial residual attention module into each basic residual block to further strengthen the efficiency of the network. Considering the continuity of terrain features, terrain weight modules are integrated into the loss module. Based on two large-scale datasets, our ERFFN shows a 10\u201320% reduction in the mean absolute error and the lowest error in terrain features, such as slope, demonstrating the superiority of an ERFFN-based DEM SR over state-of-the-art methods. Finally, to demonstrate potential value in real-world applications, we deploy the ERFFN to reconstruct a large geographic area covering 44,000 km2 which contains missing parts.<\/jats:p>","DOI":"10.3390\/rs15041038","type":"journal-article","created":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T01:38:18Z","timestamp":1676425098000},"page":"1038","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["An Enhanced Residual Feature Fusion Network Integrated with a Terrain Weight Module for Digital Elevation Model Super-Resolution"],"prefix":"10.3390","volume":"15","author":[{"given":"Guodong","family":"Chen","sequence":"first","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yumin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"},{"name":"Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"},{"name":"Key Laboratory of Digital Cartography and Land Information Application, Ministry of Natural Resources of People\u2019s Republic of China, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5969-0729","authenticated-orcid":false,"given":"John P.","family":"Wilson","sequence":"additional","affiliation":[{"name":"Spatial Sciences Institute, University of Southern California, Los Angeles, CA 90089, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Annan","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuejun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng","family":"Su","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/S0378-4754(97)00015-3","article-title":"Scale Dependence in Terrain Analysis","volume":"43","author":"Gallant","year":"1997","journal-title":"Math. Comput. Simul."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1080\/13658816.2012.739690","article-title":"A Scale-Adaptive DEM for Multi-Scale Terrain Analysis","volume":"27","author":"Chen","year":"2013","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1191\/0309133306pp492ra","article-title":"Causes and Consequences of Error in Digital Elevation Models","volume":"30","author":"Fisher","year":"2006","journal-title":"Prog. Phys. Geogr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107706","DOI":"10.1016\/j.geomorph.2021.107706","article-title":"Effects of DEM Resolutions on Soil Erosion Prediction Using Chinese Soil Loss Equation","volume":"384","author":"Li","year":"2021","journal-title":"Geomorphology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.isprsjprs.2013.08.006","article-title":"Urban DEM Generation, Analysis and Enhancements Using TanDEM-X","volume":"85","author":"Rossi","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, Y., Mo, D., Zhang, Y., and Li, X. (2017). Direct Digital Surface Model Generation by Semi-Global Vertical Line Locus Matching. Remote Sens., 9.","DOI":"10.3390\/rs9030214"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107025","DOI":"10.1016\/j.ecolind.2020.107025","article-title":"Sensitivity Analysis of Relationships between Hydrograph Components and Landscapes Metrics Extracted from Digital Elevation Models with Different Spatial Resolutions","volume":"121","author":"Sadeghi","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.isprsjprs.2019.02.008","article-title":"Deep Gradient Prior Network for DEM Super-Resolution: Transfer Learning from Image to DEM","volume":"150","author":"Xu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.isprsjprs.2022.04.028","article-title":"Terrain Feature-Aware Deep Learning Network for Digital Elevation Model Superresolution","volume":"189","author":"Zhang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"247","DOI":"10.5194\/isprs-archives-XLI-B3-247-2016","article-title":"Convolutional Neural Network Based Dem Super Resolution","volume":"XLI-B3","author":"Chen","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1080\/14498596.2011.623348","article-title":"The Inverse Distance Weighted Interpolation Method and Error Propagation Mechanism\u2014Creating a DEM from an Analogue Topographical Map","volume":"56","author":"Achilleos","year":"2011","journal-title":"J. Spat. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1080\/014311600210957","article-title":"The Accuracy of Digital Elevation Models Interpolated to Higher Resolutions","volume":"21","author":"Rees","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.isprsjprs.2017.09.014","article-title":"DEM Generation from Contours and a Low-Resolution DEM","volume":"134","author":"Li","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.isprsjprs.2016.11.002","article-title":"High-Quality Seamless DEM Generation Blending SRTM-1, ASTER GDEM v2 and ICESat\/GLAS Observations","volume":"123","author":"Yue","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1080\/13658816.2013.794281","article-title":"The Recent Advancement in Digital Terrain Analysis and Modeling","volume":"27","author":"Zhou","year":"2013","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., and Tang, X. (2014, January 6\u201312). Learning a Deep Convolutional Network for Image Super-Resolution. Proceedings of the ECCV: 13th European Conference, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Lee, K.M. (2017, January 21\u201326). Enhanced Deep Residual Networks for Single Image Super-Resolution. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhang, W., Tang, Y., Tang, J., and Wu, G. (2020, January 13\u201319). Residual Feature Aggregation Network for Image Super-Resolution. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00243"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., and Fu, Y. (2018, January 18\u201323). Residual Dense Network for Image Super-Resolution. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00262"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Qing, Y., and Liu, W. (2021). Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism. Remote Sens., 13.","DOI":"10.3390\/rs13030335"},{"key":"ref_21","first-page":"102543","article-title":"Image Super-Resolution with Dense-Sampling Residual Channel-Spatial Attention Networks for Multi-Temporal Remote Sensing Image Classification","volume":"104","author":"Zhu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (July, January 26). Accurate Image Super-Resolution Using Very Deep Convolutional Networks. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (July, January 26). Deeply-Recursive Convolutional Network for Image Super-Resolution. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.181"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016, January 11\u201314). Accelerating the Super-Resolution Convolutional Neural Network. Proceedings of the Computer Vision\u2014ECCV 2016: 14th European Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46487-9"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., and Wang, Z. (July, January 26). Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., and Chua, T.-S. (2017, January 21\u201326). SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.667"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201323). Non-Local Neural Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_29","unstructured":"Bach, F., and Blei, D. (2015, January 6\u201311). Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018, January 8\u201314). Image Super-Resolution Using Very Deep Residual Channel Attention Networks. Proceedings of the Computer Vision\u2014ECCV 2018: 15th European Conference, Munich, Germany.","DOI":"10.1007\/978-3-030-01225-0"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3911","DOI":"10.1109\/TCSVT.2019.2915238","article-title":"Channel-Wise and Spatial Feature Modulation Network for Single Image Super-Resolution","volume":"30","author":"Hu","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dai, T., Cai, J., Zhang, Y., Xia, S.-T., and Zhang, L. (2019, January 15\u201320). Second-Order Attention Network for Single Image Super-Resolution. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01132"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Jiang, L., Hu, Y., Xia, X., Liang, Q., Soltoggio, A., and Kabir, S.R. (2020). A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs. Water, 12.","DOI":"10.3390\/w12051369"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"112818","DOI":"10.1016\/j.rse.2021.112818","article-title":"Integrating Topographic Knowledge into Deep Learning for the Void-Filling of Digital Elevation Models","volume":"269","author":"Li","year":"2022","journal-title":"Remote Sens Environ."},{"key":"ref_35","first-page":"178","article-title":"Super-Resolution Reconstruction of DEM in Mountain Area Based on Deep Residual Network","volume":"52","author":"Zhang","year":"2021","journal-title":"Nongye Jixie Xuebao\/Trans. Chin. Soc. Agric. Mach."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, R., Bian, S., and Li, H. (2021). RSPCN: Super-Resolution of Digital Elevation Model Based on Recursive Sub-Pixel Convolutional Neural Networks. ISPRS Int. J. Geoinf., 10.","DOI":"10.3390\/ijgi10080501"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1080\/13658816.2019.1599122","article-title":"Spatial Interpolation Using Conditional Generative Adversarial Neural Networks","volume":"34","author":"Zhu","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8373","DOI":"10.1109\/JSTARS.2021.3105123","article-title":"Real-World DEM Super-Resolution Based on Generative Adversarial Networks for Improving InSAR Topographic Phase Simulation","volume":"14","author":"Wu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","first-page":"1593","article-title":"Extracting Topographic Structure from Digital Elevation Data for Geographic Information-System Analysis","volume":"54","author":"Jenson","year":"1988","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image Super-Resolution Using Deep Convolutional Networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhou, A., Chen, Y., Wilson, J.P., Su, H., Xiong, Z., and Cheng, Q. (2021). An Enhanced Double-Filter Deep Residual Neural Network for Generating Super Resolution DEMs. Remote Sens., 13.","DOI":"10.3390\/rs13163089"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1038\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:35:14Z","timestamp":1760121314000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1038"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,14]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15041038"],"URL":"https:\/\/doi.org\/10.3390\/rs15041038","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,14]]}}}