{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:43:33Z","timestamp":1778258613824,"version":"3.51.4"},"reference-count":77,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,4,17]],"date-time":"2019-04-17T00:00:00Z","timestamp":1555459200000},"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":["61772400, 61772399, 61501353, 61573267, 61801351"],"award-info":[{"award-number":["61772400, 61772399, 61501353, 61573267, 61801351"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"UK EPSRC","award":["Grants EP\/N508664\/1, EP\/R007187\/1 and EP\/N011074\/1"],"award-info":[{"award-number":["Grants EP\/N508664\/1, EP\/R007187\/1 and EP\/N011074\/1"]}]},{"name":"Royal Society-Newton Advanced Fellowship","award":["Grant NA160342"],"award-info":[{"award-number":["Grant NA160342"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Aerial photographs and satellite images are one of the resources used for earth observation. In practice, automated detection of roads on aerial images is of significant values for the application such as car navigation, law enforcement, and fire services. In this paper, we present a novel road extraction method from aerial images based on an improved generative adversarial network, which is an end-to-end framework only requiring a few samples for training. Experimental results on the Massachusetts Roads Dataset show that the proposed method provides better performance than several state of the art techniques in terms of detection accuracy, recall, precision and F1-score.<\/jats:p>","DOI":"10.3390\/rs11080930","type":"journal-article","created":{"date-parts":[[2019,4,17]],"date-time":"2019-04-17T07:58:09Z","timestamp":1555487889000},"page":"930","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["Aerial Image Road Extraction Based on an Improved Generative Adversarial Network"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0379-2042","authenticated-orcid":false,"given":"Xiangrong","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Han","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Li","sequence":"additional","affiliation":[{"name":"Computer Science Department, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1375-0778","authenticated-orcid":false,"given":"Xu","family":"Tang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Leicester, Leicester LE1 7RH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Licheng","family":"Jiao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Resende, M., Jorge, S., Longhitano, G., and Quintanilha, J.A. (2008, January 7\u201311). Use of Hyperspectral and High Spatial Resolution Image Data in an Asphalted Urban Road Extraction. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA.","DOI":"10.1109\/IGARSS.2008.4779603"},{"key":"ref_2","first-page":"34","article-title":"Hyperspectral RS image road feature extraction based on SVM","volume":"5","author":"Shen","year":"2012","journal-title":"J. Chang\u2019an Univ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1109\/36.662728","article-title":"Detection of linear features in SAR images: Application to road network extraction","volume":"36","author":"Tupin","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","first-page":"1","article-title":"Road Segmentation in SAR Satellite Images with Deep Fully Convolutional Neural Networks","volume":"99","author":"Corentin","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3611","DOI":"10.1109\/TGRS.2017.2677260","article-title":"Application of Multitemporal InSAR Covariance and Information Fusion to Robust Road Extraction","volume":"55","author":"Jiang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","first-page":"226","article-title":"Automatic extraction of road features in urban environments using dense ALS data","volume":"64","author":"Riveiro","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s12524-009-0023-9","article-title":"Automatic urban road extraction using airborne laser scanning\/altimetry and high resolution satellite data","volume":"37","author":"Tiwari","year":"2009","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_8","first-page":"119","article-title":"Feasibility Of Multispectral Airborne Laser Scanning for Land Cover Classification, Road Mapping And Map Updating","volume":"3","author":"Matikainen","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.autcon.2017.09.004","article-title":"Automatic classification of urban ground elements from mobile laser scanning data","volume":"86","author":"Balado","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1109\/TITS.2014.2328589","article-title":"Automated Road Information Extraction from Mobile Laser Scanning Data","volume":"16","author":"Guan","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2013.11.005","article-title":"Using mobile laser scanning data for automated extraction of road markings","volume":"87","author":"Guan","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8331","DOI":"10.1080\/01431161.2010.540587","article-title":"Semi-automatic road tracking by template matching and distance transformation in urban areas","volume":"32","author":"Zhang","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","first-page":"224","article-title":"Semiautomatic extraction of ribbon roads from high resolution remotely sensed imagery based on angular texture signature and profile match","volume":"12","author":"Zhang","year":"2008","journal-title":"J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Coulibaly, I., Spiric, N., Sghaier, M.O., Manzo-Vargas, W., Lepage, R., and St.-Jacques, M. (2014, January 13\u201318). Road extraction from high resolution remote sensing image using multiresolution in case of major disaster. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6947035"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gaetano, R., Zerubia, J., Scarpa, G., and Poggi, G. (2011, January 6\u20138). Morphological road segmentation in urban areas from high resolution satellite images. Proceedings of the 17th International Conference on Digital Signal Processing, Corfu, Greece.","DOI":"10.1109\/ICDSP.2011.6005015"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4441","DOI":"10.1109\/TGRS.2012.2190078","article-title":"Road network detection using probabilistic and graph theoretical methods","volume":"50","author":"Unsalan","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","first-page":"301","article-title":"To extract roads with no clear and continuous boundaries in RS images","volume":"37","author":"Zhou","year":"2008","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_18","first-page":"144","article-title":"Statistical edge detectors applied to SAR images","volume":"3","author":"Airouche","year":"2008","journal-title":"Int. J. Comput. Commun. Control"},{"key":"ref_19","first-page":"269","article-title":"An optimization method for road nets using improved hough transform with width-tolerant","volume":"31","author":"Zhang","year":"2014","journal-title":"J. Geomat. Sci. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.isprsjprs.2015.01.013","article-title":"Water flow based geometric active deformable model for road network","volume":"102","author":"Leninisha","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Anil, P.N., and Natarajan, S. (2010, January 11\u201314). A novel approach using active contour model for semi-automatic road extraction from high resolution satellite imagery. Proceedings of the International Conference on Machine Learning and Cybernetics, Qingdao, China.","DOI":"10.1109\/ICMLC.2010.36"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/BF00133570","article-title":"Snakes: Active contour models","volume":"1","author":"Kass","year":"1988","journal-title":"Int. J. Comput. Vis."},{"key":"ref_23","first-page":"1040","article-title":"Road extraction in quaternion space from high spatial resolution remotely sensed images basing on GVF snake model","volume":"15","author":"Tang","year":"2011","journal-title":"J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1109\/JSTARS.2012.2199085","article-title":"Semi-automated road detection from high resolution satellite images by directional morphological enhancement and segmentation techniques","volume":"5","author":"Chaudhuri","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","first-page":"26","article-title":"Automatic recognition of landscape linear features from high-resolution satellite images","volume":"7","author":"Cheng","year":"2003","journal-title":"J. Remote Sens."},{"key":"ref_26","first-page":"509","article-title":"An extended phase field higher-order active contour model for networks and its application to road network extraction from VHR satellite images","volume":"88","author":"Peng","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_27","first-page":"93","article-title":"Automatic road extraction based on multi-scale modeling, context, and snakes","volume":"95","author":"Laptev","year":"1997","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shen, Z., Luo, J., and Gao, L. (2010, January 25\u201330). Road extraction from high-resolution remotely sensed panchromatic image in different research scales. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5649912"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1080\/01431160802546837","article-title":"Road centreline extraction from high-resolution imagery based on multiscale structural features and support vector machines","volume":"30","author":"Huang","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yi, W., Chen, Y., Tang, H., and Deng, L. (2010, January 25\u201330). Experimental research on urban road extraction from high-resolution rs images using probabilistic topic models. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5650966"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hedman, K., Hinz, S., and Stilla, U. (2007, January 11\u201313). Road extraction from SAR multi-aspect data supported by a statistical context-based fusion. Proceedings of the Urban Remote Sensing Joint Event, Paris, France.","DOI":"10.1109\/URS.2007.371874"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3906","DOI":"10.1109\/TGRS.2011.2136381","article-title":"Use of salient features for the design of a multistage framework to extract roads from high-resolution multispectral satellite images","volume":"49","author":"Das","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"8074","DOI":"10.1080\/01431161.2014.978956","article-title":"A two-stage framework for road extraction from high-resolution satellite images by using prominent features of impervious surfaces","volume":"35","author":"Singh","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3359","DOI":"10.1109\/TGRS.2013.2272593","article-title":"An integrated method for urban main-road centerline extraction from optical remotely sensed imagery","volume":"52","author":"Shi","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1109\/LGRS.2006.873875","article-title":"Improving urban road extraction in high-resolution images exploiting directional filtering, perceptual grouping, and simple topological concepts","volume":"3","author":"Gamba","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/BF02826642","article-title":"Automatic road change detection and GIS updating from high spatial remotely-sensed imagery","volume":"7","author":"Zhang","year":"2004","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chen, H., Yin, L., and Ma, L. (2014, January 11\u201314). Research on road information extraction from high resolution imagery based on global precedence. Proceedings of the 2014 3rd International Workshop on Earth Observation and Remote Sensing Applications, Changsha, China.","DOI":"10.1109\/EORSA.2014.6927868"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2807","DOI":"10.1109\/TGRS.2010.2041783","article-title":"Road extraction from satellite images using particle filtering and extended Kalman filtering","volume":"48","author":"Movaghati","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2014.06.006","article-title":"Tensor-cuts: A simultaneous multi-type feature extractor and classifier and its application to road extraction from satellite images","volume":"95","author":"Poullis","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4853","DOI":"10.1109\/JSTARS.2015.2443552","article-title":"An object-based method for road network extraction in VHR satellite images","volume":"8","author":"Miao","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1946","DOI":"10.1109\/JSTARS.2015.2449296","article-title":"Road extraction from very high resolution remote sensing optical images based on texture analysis and beamlet transform","volume":"9","author":"Sghaier","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3737","DOI":"10.1109\/TGRS.2014.2382566","article-title":"Regular shape similarity index: A novel index for accurate extraction of regular objects from remote sensing images","volume":"53","author":"Sun","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4785","DOI":"10.1109\/JSTARS.2015.2477097","article-title":"A direction-guided ant colony optimization method for extraction of urban road information from very-high-resolution images","volume":"8","author":"Yin","year":"2015","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"ref_44","first-page":"217","article-title":"Region-based urban road extraction from VHR satellite images using binary partition tree","volume":"44","author":"Li","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neucom.2016.03.095","article-title":"Road centerlines extraction from high resolution images based on an improved directional segmentation and road probability","volume":"212","author":"Liu","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.patrec.2016.05.014","article-title":"Morphological path filtering at the region scale for efficient and robust road network extraction from satellite imagery","volume":"83","author":"Courtrai","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.jvcir.2016.06.024","article-title":"Improved road centerlines extraction in high-resolution remote sensing images using shear transform, directional morphological filtering and enhanced broken lines connection","volume":"40","author":"Liu","year":"2016","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.neucom.2016.04.026","article-title":"Accurate urban road centerline extraction from VHR imagery via multiscale segmentation and tensor voting","volume":"205","author":"Cheng","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.isprsjprs.2016.04.003","article-title":"Road centerline extraction from airborne lidar point cloud based on hierarchical fusion and optimization","volume":"118","author":"Hui","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). Imagenet Classification with Deep Convolutional Neural Networks. Proceedings of the Neural Information Processing Systems Conference, Lake Tahoe, NV, USA."},{"key":"ref_51","unstructured":"Srivastava, N., Mansimov, E., and Salakhutdinov, R. (2015, January 6\u201311). Unsupervised learning of video representations using lstms. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_52","unstructured":"Mnih, V., and Hinton, G.E. (2010, January 5\u201311). Learning to detect roads in high-resolution aerial images. Proceedings of the 11th European Conference on Computer Vision. Springer: Berlin, Heidelberg, Germany."},{"key":"ref_53","unstructured":"Mnih, V., and Hinton, G. (July, January 26). Learning to label aerial images from noisy data. Proceedings of the International Conference on Machine Learning, Edinburgh, Scotland."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3322","DOI":"10.1109\/TGRS.2017.2669341","article-title":"Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network","volume":"55","author":"Cheng","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/TGRS.2018.2864987","article-title":"Scene Classification with Recurrent Attention of VHR Remote Sensing Images","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Maturana, D., and Scherer, S. (2015, January 26\u201330). 3D Convolutional Neural Networks for landing zone detection from LiDAR. Proceedings of the IEEE International Conference on Robotics & Automation, Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139679"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"67","DOI":"10.3390\/rs9010067","article-title":"Spectral\u2013Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network","volume":"9","author":"Ying","year":"2017","journal-title":"Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 8\u201310). 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_60","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Li, J., Cui, W., and Jiang, H. (2016, January 10\u201315). Fully convolutional networks for building and road extraction: Preliminary results. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729406"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","article-title":"Convolutional neural networks for large-scale remote-sensing image classification","volume":"55","author":"Maggiori","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","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 nets. Proceedings of the Neural Information Processing Systems Conference, Montr\u00e9al, QC, Canada."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"25486","DOI":"10.1109\/ACCESS.2017.2773142","article-title":"Road detection from remote sensing images by generative adversarial networks","volume":"6","author":"Shi","year":"2018","journal-title":"IEEE Access"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Costea, D., Marcu, A., Leordeanu, M., and Slusanschi, E. (2017, January 22\u201329). Creating roadmaps in aerial images with generative adversarial networks and smoothing-based optimization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.246"},{"key":"ref_66","unstructured":"Radford, A., Metz, L., and Chintala, S. (2016, January 2\u20134). Unsupervised representation learning with deep convolutional generative adversarial networks. Proceedings of the International Conference on Learning Representations, San Juan, Puerto Rico."},{"key":"ref_67","unstructured":"Mirza, M., and Osindero, S. (arXiv, 2014). Conditional generative adversarial nets, arXiv."},{"key":"ref_68","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (arXiv, 2017). Wasserstein GAN, arXiv."},{"key":"ref_69","unstructured":"Arjovsky, M., and Bottou, L. (arXiv, 2017). Towards principled methods for training generative adversarial networks, arXiv."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_71","unstructured":"Kim, T., Cha, M., Kim, H., Lee, J.K., and Kim, J. (2017, January 6\u201311). Learning to discover cross-domain relations with generative adversarial networks. Proceedings of the International Conference on Machine Learning, Sydney, NSW, Australia."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Yi, Z., Zhang, H., Tan, P., and Gong, M. (2017, January 22\u201329). Dualgan: Unsupervised dual learning for image-to-image translation. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.310"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 22\u201325). 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_74","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_75","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_76","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0893-6080(98)00116-6","article-title":"On the momentum term in gradient descent learning algorithms","volume":"12","author":"Qian","year":"1999","journal-title":"Neural Netw."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Boulanger-Lewandowski, N., and Pascanu, R. (2013, January 26\u201330). Advances in optimizing recurrent networks. Proceedings of the 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6639349"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/8\/930\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:46:07Z","timestamp":1760186767000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/8\/930"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,17]]},"references-count":77,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["rs11080930"],"URL":"https:\/\/doi.org\/10.3390\/rs11080930","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,17]]}}}