{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:41:44Z","timestamp":1777657304055,"version":"3.51.4"},"reference-count":55,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,18]],"date-time":"2022-05-18T00:00:00Z","timestamp":1652832000000},"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":["42001340"],"award-info":[{"award-number":["42001340"]}],"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":["KF-2020-05-068"],"award-info":[{"award-number":["KF-2020-05-068"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources","award":["42001340"],"award-info":[{"award-number":["42001340"]}]},{"name":"Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources","award":["KF-2020-05-068"],"award-info":[{"award-number":["KF-2020-05-068"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Resolution is a comprehensive reflection and evaluation index for the visual quality of remote sensing images. Super-resolution processing has been widely applied for extracting information from remote sensing images. Recently, deep learning methods have found increasing application in the super-resolution processing of remote sensing images. However, issues such as blurry object edges and existing artifacts persist. To overcome these issues, this study proposes an improved generative adversarial network with self-attention and texture enhancement (TE-SAGAN) for remote sensing super-resolution images. We first designed an improved generator based on the residual dense block with a self-attention mechanism and weight normalization. The generator gains the feature extraction capability and enhances the training model stability to improve edge contour and texture. Subsequently, a joint loss, which is a combination of L1-norm, perceptual, and texture losses, is designed to optimize the training process and remove artifacts. The L1-norm loss is designed to ensure the consistency of low-frequency pixels; perceptual loss is used to entrench medium- and high-frequency details; and texture loss provides the local features for the super-resolution process. The results of experiments using a publicly available dataset (UC Merced Land Use dataset) and our dataset show that the proposed TE-SAGAN yields clear edges and textures in the super-resolution reconstruction of remote sensing images.<\/jats:p>","DOI":"10.3390\/rs14102425","type":"journal-article","created":{"date-parts":[[2022,5,18]],"date-time":"2022-05-18T23:14:26Z","timestamp":1652915666000},"page":"2425","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["TE-SAGAN: An Improved Generative Adversarial Network for Remote Sensing Super-Resolution Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7421-4915","authenticated-orcid":false,"given":"Yongyang","family":"Xu","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"},{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"},{"name":"Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna","family":"Hu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhong","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuejing","family":"Xie","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liufeng","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wu, L., Xie, Z., and Chen, Z. (2018). Building extraction in very high resolution remote sensing imagery using deep learning and guided filters. Remote Sens., 10.","DOI":"10.3390\/rs10010144"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xu, Y., Xie, Z., Feng, Y., and Chen, Z. (2018). Road extraction from high-resolution remote sensing imagery using deep learning. Remote Sens., 10.","DOI":"10.3390\/rs10091461"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Guo, M., Liu, H., Xu, Y., and Huang, Y. (2020). Building extraction based on U-Net with an attention block and multiple losses. Remote Sens., 12.","DOI":"10.3390\/rs12091400"},{"key":"ref_4","first-page":"5602411","article-title":"Graph convolutional networks for the automated production of building vector maps from aerial images","volume":"60","author":"Wei","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8352","DOI":"10.1080\/01431161.2020.1775322","article-title":"Refined extraction of buildings with the semantic edge-assisted approach from very high-resolution remotely sensed imagery","volume":"41","author":"Xia","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/ICIP.1995.537535","article-title":"Reconstruction of a High-Resolution Image by Simultaneous Registration, Restoration, and Interpolation of Low-Resolution Images","volume":"2","author":"Tom","year":"1995","journal-title":"Proc. Int. Conf. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1964","DOI":"10.1109\/TGRS.2005.853569","article-title":"Resolution Enhancement of Multilook Imagery for the Multispectral Thermal Imager","volume":"43","author":"Galbraith","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.imavis.2008.05.010","article-title":"A Soft MAP Framework for Blind Super-Resolution Image Reconstruction","volume":"27","author":"He","year":"2009","journal-title":"Image Vis. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6792","DOI":"10.1109\/TGRS.2018.2843525","article-title":"A New Deep Generative Network for Unsupervised Remote Sensing Single-Image Super-Resolution","volume":"56","author":"Haut","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3633","DOI":"10.1109\/TGRS.2019.2959020","article-title":"Coupled Adversarial Training for Remote Sensing Image Super-Resolution","volume":"58","author":"Lei","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","first-page":"317","article-title":"Multiframe Image Restoration and Registration","volume":"1","author":"Tsai","year":"1984","journal-title":"Adv. Comput. Vis. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest Neighbor Pattern Classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1175\/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2","article-title":"Lanczos Filtering in One and Two Dimensions","volume":"18","author":"Duchon","year":"1979","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/0021-9045(73)90050-6","article-title":"Error Bounds for Bicubic Spline Interpolation","volume":"7","author":"Carlson","year":"1973","journal-title":"J. Approx. Theory"},{"key":"ref_15","unstructured":"Miles, N. (1994). Method of Recovering Tomographic Signal Elements in a Projection Profile or Image by Solving Linear Equations. (No. 5323007), JUSTIA Patents."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1914","DOI":"10.1364\/JOSAA.9.001914","article-title":"Projection-Based Image Restoration","volume":"9","author":"Stark","year":"1992","journal-title":"J. Opt. Soc. Am. A-Opt. Image Sci. Vis."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1715","DOI":"10.1364\/JOSAA.6.001715","article-title":"High-Resolution Image Recovery from Image-Plane Arrays, Using Convex Projections","volume":"6","author":"Stark","year":"1989","journal-title":"J. Opt. Soc. Am. A Opt. Image Sci."},{"key":"ref_18","first-page":"115","article-title":"Super Resolution from Image Sequences Super-Resolution through Neighbor Embedding","volume":"2","author":"Irani","year":"1990","journal-title":"CVPR"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/34.75515","article-title":"Fast B-Spline Transforms for Continuous Image Representation and Interpolation","volume":"13","author":"Unser","year":"1991","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1109\/78.193220","article-title":"B-Spline Signal Processing: Part I\u2014Theory","volume":"41","author":"Unser","year":"1993","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/78.193221","article-title":"B-Spline Signal Processing: Part II-Efficient Design and Applications","volume":"41","author":"Unser","year":"1993","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xu, Y., Jin, S., Chen, Z., Xie, X., Hu, S., and Xie, Z. (2022). Application of a graph convolutional network with visual and semantic features to classify urban scenes. Int. J. Geogr. Inf. Sci., 1\u201326.","DOI":"10.1080\/13658816.2022.2048834"},{"key":"ref_23","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 2014, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref_24","first-page":"1486","article-title":"Deep Generative Image Models Using a Laplacian Pyramid of Adversarial Networks","volume":"28","author":"Denton","year":"2015","journal-title":"NIPS"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016, January 11\u201314). Perceptual Losses for Real-Time Style Transfer and Super-Resolution. Proceedings of the Computer Vision\u2014ECCV 2016, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46478-7"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (2016, January 27\u201330). 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_27","doi-asserted-by":"crossref","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018, January 8\u201314). Multi-Scale Residual Network for Image Super-Resolution. Proceedings of the Computer Vision\u2014ECCV 2018, Munich, Germany.","DOI":"10.1007\/978-3-030-01216-8"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Han, W., Chang, S., Liu, D., Yu, M., Witbrock, M., and Huang, T.S. (2018, January 18\u201323). Image Super-Resolution via Dual-State Recurrent Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00178"},{"key":"ref_29","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_30","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_31","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_32","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X., and Xu, C. (2017, January 22\u201329). MemNet: A Persistent Memory Network for Image Restoration. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.486"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tong, T., Li, G., Liu, X., and Gao, Q. (2017, January 22\u201329). Image Super-Resolution Using Dense Skip Connections. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.514"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Aitken, A.P., Tejani, A., Totz, J., Wang, Z., and Shi, W. (2017, January 21\u201326). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Loy, C.C., Qiao, Y., and Tang, X. (2018, January 8\u201314). ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. Proceedings of the ECCV Workshops 2018, Munich, Germany.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Rakotonirina, N.C., and Rasoanaivo, A. (2020, January 4\u20138). ESRGAN+: Further Improving Enhanced Super-Resolution Generative Adversarial Network. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9054071"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, X., Xie, L., Dong, C., and Shan, Y. (2021, January 10). Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00217"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jo, Y., Yang, S., and Kim, S.J. (2020, January 14\u201319). Investigating Loss Functions for Extreme Super-Resolution. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00220"},{"key":"ref_39","unstructured":"Salimans, T., and Kingma, D.P. (2017, January 4\u20139). Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks. Proceedings of the 30th International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_40","first-page":"2672","article-title":"Generative Adversarial Nets","volume":"Volume 2","author":"Goodfellow","year":"2020","journal-title":"Proceedings of the 27th International Conference on Neural Information Processing Systems"},{"key":"ref_41","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_42","first-page":"448","article-title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","volume":"Volume 37","author":"Ioffe","year":"2015","journal-title":"Proceedings of the 32nd International Conference on International Conference on Machine Learning"},{"key":"ref_43","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_45","unstructured":"Jolicoeur-Martineau, A. (2018). The Relativistic Discriminator: A Key Element Missing from Standard GAN. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image Quality Assessment: From Error Visibility to Structural Similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_47","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017, January 4\u20139). GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ma, W., Pan, Z., Guo, J., and Lei, B. (2018, January 22\u201327). Super-resolution of remote sensing images based on transferred generative adversarial network. Proceedings of the IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517442"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Tian, Y., Li, J., and Xu, Y. (2022). Unsupervised Remote Sensing Image Super-Resolution Guided by Visible Images. Remote Sens., 14.","DOI":"10.3390\/rs14061513"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Guo, M., Zhang, Z., Liu, H., and Huang, Y. (2022). NDSRGAN: A Novel Dense Generative Adversarial Network for Real Aerial Imagery Super-Resolution Reconstruction. Remote Sens., 14.","DOI":"10.3390\/rs14071574"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2010, January 2\u20135). Bag-of-Visual-Words and Spatial Extensions for Land-Use Classification. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, New York, NY, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_53","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. Int. Conf. Learn. Represent."},{"key":"ref_54","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_55","first-page":"41","article-title":"Residual Feature Distillation Network for Lightweight Image Super-Resolution","volume":"Volume 12537","author":"Bartoli","year":"2020","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2020 Workshops"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2425\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:14:22Z","timestamp":1760138062000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2425"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,18]]},"references-count":55,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14102425"],"URL":"https:\/\/doi.org\/10.3390\/rs14102425","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,18]]}}}