{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T08:43:34Z","timestamp":1783586614477,"version":"3.55.0"},"reference-count":56,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T00:00:00Z","timestamp":1648080000000},"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":["41971356, 41701446"],"award-info":[{"award-number":["41971356, 41701446"]}],"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":["KF-2020-05-011"],"award-info":[{"award-number":["KF-2020-05-011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, more and more researchers have used deep learning methods for super-resolution reconstruction and have made good progress. However, most of the existing super-resolution reconstruction models generate low-resolution images for training by downsampling high-resolution images through bicubic interpolation, and the models trained from these data have poor reconstruction results on real-world low-resolution images. In the field of unmanned aerial vehicle (UAV) aerial photography, the use of existing super-resolution reconstruction models in reconstructing real-world low-resolution aerial images captured by UAVs is prone to producing some artifacts, texture detail distortion and other problems, due to compression and fusion processing of the aerial images, thereby resulting in serious loss of texture detail in the obtained low-resolution aerial images. To address this problem, this paper proposes a novel dense generative adversarial network for real aerial imagery super-resolution reconstruction (NDSRGAN), and we produce image datasets with paired high- and low-resolution real aerial remote sensing images. In the generative network, we use a multilevel dense network to connect the dense connections in a residual dense block. In the discriminative network, we use a matrix mean discriminator that can discriminate the generated images locally, no longer discriminating the whole input image using a single value but instead in chunks of regions. We also use smoothL1 loss instead of the L1 loss used in most existing super-resolution models, to accelerate the model convergence and reach the global optimum faster. Compared with traditional models, our model can better utilise the feature information in the original image and discriminate the image in patches. A series of experiments is conducted with real aerial imagery datasets, and the results show that our model achieves good performance on quantitative metrics and visual perception.<\/jats:p>","DOI":"10.3390\/rs14071574","type":"journal-article","created":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T23:31:43Z","timestamp":1648164703000},"page":"1574","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["NDSRGAN: A Novel Dense Generative Adversarial Network for Real Aerial Imagery Super-Resolution Reconstruction"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4097-4814","authenticated-orcid":false,"given":"Mingqiang","family":"Guo","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"},{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"National Engineering Research Center of Geographic Information System, Wuhan 430074, China"},{"name":"Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6251-2306","authenticated-orcid":false,"given":"Zeyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"},{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"National Engineering Research Center of Geographic Information System, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3794-629X","authenticated-orcid":false,"given":"Heng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"National Engineering Research Center of Geographic Information System, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Huang","sequence":"additional","affiliation":[{"name":"Wuhan Zondy Advanced Technology Institute Co., Ltd., Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,24]]},"reference":[{"key":"ref_1","unstructured":"Walter, V. (1999, January 22\u201326). Automated GIS data collection and update. Proceedings of the Photogrammetric Week\u2032 99, Heidelberg, Germany."},{"key":"ref_2","unstructured":"Lee, K., and Ryu, H.Y. (2004, January 20\u201324). Automatic circuity and accessibility extraction by road graph network and its application with high-resolution satellite imagery. Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"397","DOI":"10.7848\/ksgpc.2015.33.5.397","article-title":"Digital map updates with UAV photogrammetric methods","volume":"33","author":"Lim","year":"2015","journal-title":"J. Korean Soc. Surv. Geod. Photogramm. Cartogr."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/LGRS.2011.2161569","article-title":"Automatic target detection in high-resolution remote sensing images using spatial sparse coding bag-of-words model","volume":"9","author":"Sun","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"705021","DOI":"10.3389\/fpls.2021.705021","article-title":"Multi-target recognition of bananas and automatic positioning for the inflorescence axis cutting point","volume":"12","author":"Wu","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.istruc.2021.12.055","article-title":"Seismic performance evaluation of recycled aggregate concrete-filled steel tubular columns with field strain detected via a novel mark-free vision method","volume":"37","author":"Tang","year":"2022","journal-title":"Structures"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.neucom.2019.03.106","article-title":"Ultra-dense GAN for satellite imagery super-resolution","volume":"398","author":"Wang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_9","unstructured":"Forsyth, D., Ponce, J., Mukherjee, S., and Bhattacharjee, A.K. (2011). Computer Vision: A Modern Approach, Prentice Hall."},{"key":"ref_10","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, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_12","unstructured":"Koester, E., and Sahin, C.S. (2019). A comparison of super-resolution and nearest neighbors interpolation applied to object detection on satellite data. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, X. (2008, January 18). A new kind of super-resolution reconstruction algorithm based on the ICM and the bilinear interpolation. Proceedings of the 2008 International Seminar on Future BioMedical Information Engineering, Wuhan, China.","DOI":"10.1109\/FBIE.2008.44"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, X. (2008, January 21\u201322). A new kind of super-resolution reconstruction algorithm based on the ICM and the bicubic interpolation. Proceedings of the 2008 International Symposium on Intelligent Information Technology Application Workshops, Shanghai, China.","DOI":"10.1109\/IITA.Workshops.2008.12"},{"key":"ref_15","first-page":"31","article-title":"Near optimal non-uniform interpolation for image super-resolution from multiple images","volume":"20","author":"Gilman","year":"2006","journal-title":"Image Vis. Comput. N. Z. Great Barrier Isl. N. Z."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Rasti, P., Demirel, H., and Anbarjafari, G. (2013, January 10\u201312). Iterative back projection based image resolution enhancement. Proceedings of the 2013 8th Iranian Conference on Machine Vision and Image Processing, Zanjan, Iran.","DOI":"10.1109\/IranianMVIP.2013.6779986"},{"key":"ref_17","unstructured":"Tipping, M.E., and Bishop, C.M. (2003, January 8\u201313). Bayesian image super-resolution. Proceedings of the Advances in Neural Information Processing Systems, Vancouver and Whistler, BC, Canada."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fan, C., Wu, C., Li, G., and Ma, J. (2017). Projections onto convex sets super-resolution reconstruction based on point spread function estimation of low-resolution remote sensing images. Sensors, 17.","DOI":"10.3390\/s17020362"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108033","DOI":"10.1016\/j.sigpro.2021.108033","article-title":"Two-direction self-learning super-resolution propagation based on neighbor embedding","volume":"183","author":"Xu","year":"2021","journal-title":"Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"115925","DOI":"10.1016\/j.image.2020.115925","article-title":"Image super-resolution reconstruction based on sparse representation and deep learning","volume":"87","author":"Zhang","year":"2020","journal-title":"Signal Process. Image Commun."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ooi, Y.K., and Ibrahim, H. (2021). Deep Learning Algorithms for Single Image Super-Resolution: A Systematic Review. Electronics, 10.","DOI":"10.3390\/electronics10070867"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/JRPROC.1961.287775","article-title":"Steps toward Artificial Intelligence","volume":"49","author":"Minsky","year":"1961","journal-title":"Proc. IRE"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3106","DOI":"10.1109\/TMM.2019.2919431","article-title":"Deep Learning for Single Image Super-Resolution: A Brief Review","volume":"21","author":"Yang","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_24","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":"2015","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation Applied to Handwritten Zip Code Recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., and Tang, X. (2016, January 8\u201316). Accelerating the super-resolution convolutional neural network. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"ref_27","unstructured":"Xu, L., Ren, J.S., Liu, C., and Jia, J. (2014, January 8\u201313). Deep Convolutional Neural Network for Image Deconvolution. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Mu Lee, K. (2017, January 21\u201326). Enhanced deep residual networks for single image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_30","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv."},{"key":"ref_31","unstructured":"Xiaomei, Y., and Chenghu, Z. (2000, January 24\u201328). Analysis of the complexity of remote sensing image and its role on image classification. Proceedings of the IEEE Geoscience and Remote Sensing Symposium, Honolulu, HI, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.2307\/2171725","article-title":"Epistemic Conditions for Nash Equilibrium","volume":"63","author":"Aumann","year":"1995","journal-title":"Econometrica"},{"key":"ref_33","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1002\/for.3980120802","article-title":"On the limitations of comparing mean square forecast errors","volume":"12","author":"Clements","year":"1993","journal-title":"J. Forecast."},{"key":"ref_36","unstructured":"Gatys, L.A., Ecker, A.S., and Bethge, M. (2015, January 15\u201317). Texture synthesis and the controlled generation of natural stimuli using convolutional neural networks. Proceedings of the Bernstein Conference 2015, Heidelberg, Germany."},{"key":"ref_37","unstructured":"Bruna, J., Sprechmann, P., and Lecun, Y. (2016, January 2\u20134). Super-Resolution with Deep Convolutional Sufficient Statistics. Proceedings of the International Conference on Learning Representations, San Juan, Puerto Rico."},{"key":"ref_38","unstructured":"Johnson, J., Alahi, A., and Fei-Fei, L. (, January 8\u201316). Perceptual Losses for Real-Time Style Transfer and Super-Resolution. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands."},{"key":"ref_39","unstructured":"Simonyan, K., and Zisserman, A. (2014, January 14\u201316). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations, Banff, AB, Canada."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., and Change Loy, C. (2018, January 8\u201314). Esrgan: Enhanced super-resolution generative adversarial networks. Proceedings of the European Conference on Computer Vision Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"ref_41","unstructured":"Jolicoeur-Martineau, A. (May, January 30). The relativistic discriminator: A key element missing from standard GAN. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ma, C., Rao, Y., Cheng, Y., Chen, C., Lu, J., and Zhou, J. (2020, January 13\u201319). Structure-Preserving Super Resolution With Gradient Guidance. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00779"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_44","unstructured":"Xu, B., Wang, N., Chen, T., and Li, M. (2015). Empirical evaluation of rectified activations in convolutional network. arXiv."},{"key":"ref_45","unstructured":"Maas, A.L., Hannun, A.Y., and Ng, A.Y. (2013, January 16\u201321). Rectifier nonlinearities improve neural network acoustic models. Proceedings of the 30th International Conference on Machine Learning, Atlanta, GA, USA."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_48","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yang, C., Ma, C., and Yang, M. (2014, January 6\u201312). Single-Image Super-Resolution: A Benchmark. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_25"},{"key":"ref_50","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":"Zhou","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., and Wang, O. (2018, January 18\u201323). The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wang, X., Xie, L., Dong, C., and Shan, Y. (2021, January 11\u201317). Real-esrgan: Training real-world blind super-resolution with pure synthetic data. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00217"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Agustsson, E., and Timofte, R. (2017, January 21\u201326). Ntire 2017 challenge on single image super-resolution: Dataset and study. Proceedings of the IEEE conference on computer vision and pattern recognition workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.150"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., and Alberi-Morel, M.L. (2012, January 3\u20137). Low-complexity single-image super-resolution based on nonnegative neighbor embedding. Proceedings of the British Machine Vision Conference, Surrey, UK.","DOI":"10.5244\/C.26.135"},{"key":"ref_55","unstructured":"Zeyde, R., Elad, M., and Protter, M. (2010, January 24\u201330). On single image scale-up using sparse-representations. Proceedings of the International Conference on Curves and Surfaces, Avignon, France."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Huang, J., Singh, A., and Ahuja, N. (2015, January 7\u201312). Single image super-resolution from transformed self-exemplars. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299156"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1574\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:42:42Z","timestamp":1760136162000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1574"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,24]]},"references-count":56,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14071574"],"URL":"https:\/\/doi.org\/10.3390\/rs14071574","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,24]]}}}