{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T15:13:54Z","timestamp":1771168434339,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T00:00:00Z","timestamp":1616112000000},"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":["61876010"],"award-info":[{"award-number":["61876010"]}],"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":["61806013"],"award-info":[{"award-number":["61806013"]}],"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":["61906005"],"award-info":[{"award-number":["61906005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific and Technology Program Municipal Educa361 tion Commission","award":["KM202110005028"],"award-info":[{"award-number":["KM202110005028"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Roads are important mode of transportation, which are very convenient for people\u2019s daily work and life. However, it is challenging to accuratly extract road information from a high-resolution remote sensing image. This paper presents a road extraction method for remote sensing images with a complement UNet (C-UNet). C-UNet contains four modules. Firstly, the standard UNet is used to roughly extract road information from remote sensing images, getting the first segmentation result; secondly, a fixed threshold is utilized to erase partial extracted information; thirdly, a multi-scale dense dilated convolution UNet (MD-UNet) is introduced to discover the complement road areas in the erased masks, obtaining the second segmentation result; and, finally, we fuse the extraction results of the first and the third modules, getting the final segmentation results. Experimental results on the Massachusetts Road dataset indicate that our C-UNet gets the higher results than the state-of-the-art methods, demonstrating its effectiveness.<\/jats:p>","DOI":"10.3390\/s21062153","type":"journal-article","created":{"date-parts":[[2021,3,21]],"date-time":"2021-03-21T23:47:41Z","timestamp":1616370461000},"page":"2153","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":79,"title":["C-UNet: Complement UNet for Remote Sensing Road Extraction"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0953-7341","authenticated-orcid":false,"given":"Yuewu","family":"Hou","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}]},{"given":"Zhaoying","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}]},{"given":"Ting","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}]},{"given":"Yujian","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,19]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1109\/LGRS.2009.2025059","article-title":"Unsupervised change detection in satellite images using principal component analysis and k-means clustering","volume":"6","author":"Celik","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3077","DOI":"10.1016\/j.rser.2017.10.031","article-title":"Applications of the infrared thermography in the energy audit of buildings: A review","volume":"82","author":"Lucchi","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Abdollahi, A., Pradhan, B., Shukla, N., Chakraborty, S., and Alamri, A. (2020). Deep learning approaches applied to remote sensing datasets for road extraction: A state-of-the-art review. Remote Sens., 12.","DOI":"10.3390\/rs12091444"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wegner, J.D., Montoya-Zegarra, J.A., and Schindler, K. (2013, January 23\u201328). A higher-order CRF model for road network extraction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.222"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Maurya, R., Gupta, P., and Shukla, A.S. (2011, January 3\u20135). Road extraction using k-means clustering and morphological operations. Proceedings of the 2011 International Conference on Image Information Processing, Shimla, India.","DOI":"10.1109\/ICIIP.2011.6108839"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mattyus, G., Wang, S., Fidler, S., and Urtasun, R. (2015, January 7\u201313). Enhancing road maps by parsing aerial images around the world. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.197"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cheng, G., Wang, Y., Gong, Y., Zhu, F., and Pan, C. (2014, January 27\u201330). Urban road extraction via graph cuts based probability propagation. Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7026027"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cheng, G., Wang, Y., Zhu, F., and Pan, C. (2015, January 27\u201330). Road extraction via adaptive graph cuts with multiple features. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351549"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yuan, J., Tang, S., Wang, F., and Zhang, H. (2014, January 8\u201311). A robust road segmentation method based on graph cut with learnable neighboring link weights. Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China.","DOI":"10.1109\/ITSC.2014.6957929"},{"key":"ref_11","unstructured":"Zhao, H., Kumagai, J., Nakagawa, M., and Shibasaki, R. (2002). Semi-automatic road extraction from high-resolution satellite image. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch., 34."},{"key":"ref_12","unstructured":"Gruen, A., and Li, H. (1994, January 5\u20139). Semiautomatic road extraction by dynamic programming. Proceedings of the ISPRS Commission III Symposium: Spatial Information from Digital Photogrammetry and Computer Vision. International Society for Optics and Photonics, Munich, Germany."},{"key":"ref_13","unstructured":"Adhikari, S.P., and Kim, H. (2010, January 14\u201316). Dynamic programming and curve fitting based road boundary detection. Proceedings of the 9th WSEAS International Conference on Computational Intelligence, Merida, Venezuela."},{"key":"ref_14","first-page":"985","article-title":"Semi-automatic linear feature extraction by dynamic programming and LSB-snakes","volume":"63","author":"Gruen","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_15","first-page":"777","article-title":"Automatic road extraction based on multi-scale, grouping, and context","volume":"65","author":"Baumgartner","year":"1999","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Anil, P.N., and Natarajan, S. (2010, January 9\u201311). A novel approach using active contour model for semi-automatic road extraction from high resolution satellite imagery. Proceedings of the 2010 Second International Conference on Machine Learning and Computing, Bangalore, India.","DOI":"10.1109\/ICMLC.2010.36"},{"key":"ref_17","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_18","unstructured":"Marikhu, R., Dailey, M.N., Makhanov, S., and Honda, K. (2007, January 18\u201322). A family of quadratic snakes for road extraction. Proceedings of the Asian Conference on Computer Vision, Tokyo, Japan."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4943","DOI":"10.1080\/01431161.2010.493565","article-title":"Semi-automatic extraction of road networks by least squares interlaced template matching in urban areas","volume":"32","author":"Lin","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","unstructured":"Park, S.R., and Kim, T. (2001, January 5\u20139). Semi-automatic road extraction algorithm from IKONOS images using template matching. Proceedings of the 22nd Asian Conference on Remote Sensing, Singapore."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1109\/HIS.2009.317","article-title":"Semi-automatic road extraction from high-resolution remote sensing image: Review and prospects","volume":"Volume 1","author":"Li","year":"2009","journal-title":"Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems"},{"key":"ref_22","first-page":"209","article-title":"A test of automatic road extraction approaches","volume":"Volume 36","author":"Mayer","year":"2006","journal-title":"Proceedings of the Symposium of ISPRS Commission III: Photogrammetric Computer Vision: PCV\u201906"},{"key":"ref_23","first-page":"151","article-title":"Evaluation of automatic road extraction","volume":"32","author":"Heipke","year":"1997","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_24","first-page":"271","article-title":"A review of road extraction from remote sensing images","volume":"3","author":"Wang","year":"2016","journal-title":"J. Traffic Transp. Eng."},{"key":"ref_25","first-page":"32","article-title":"Road detection from high-resolution satellite images using artificial neural networks","volume":"9","author":"Mokhtarzade","year":"2007","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1080\/13658816.2019.1696968","article-title":"Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks","volume":"34","author":"Saeedimoghaddam","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bastani, F., He, S., Abbar, S., Alizadeh, M., Balakrishnan, H., Chawla, S., Madden, S., and DeWitt, D. (2018, January 18\u201323). Roadtracer: Automatic extraction of road networks from aerial images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00496"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"9362","DOI":"10.1109\/TGRS.2019.2926397","article-title":"Multi-scale and multi-task deep learning framework for automatic road extraction","volume":"57","author":"Lu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Li, J., Cui, W., and Jiang, H. (2016, January 11\u201315). Fully convolutional networks for building and road extraction: Preliminary results. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729406"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.isprsjprs.2017.05.002","article-title":"Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks","volume":"130","author":"Alshehhi","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Varia, N., Dokania, A., and Senthilnath, J. (2018, January 18\u201321). DeepExt: A convolution neural network for road extraction using RGB images captured by UAV. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India.","DOI":"10.1109\/SSCI.2018.8628717"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"016020","DOI":"10.1117\/1.JRS.12.016020","article-title":"UFCN: A fully convolutional neural network for road extraction in RGB imagery acquired by remote sensing from an unmanned aerial vehicle","volume":"12","author":"Kestur","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Panboonyuen, T., Vateekul, P., Jitkajornwanich, K., and Lawawirojwong, S. (2017, January 21\u201323). An enhanced deep convolutional encoder-decoder network for road segmentation on aerial imagery. Proceedings of the International Conference on Computing and Information Technology, Helsinki, Finland.","DOI":"10.1007\/978-3-319-60663-7_18"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"46988","DOI":"10.1109\/ACCESS.2018.2867210","article-title":"Road extraction from a high spatial resolution remote sensing image based on richer convolutional features","volume":"6","author":"Hong","year":"2018","journal-title":"IEEE Access"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road extraction by deep residual u-net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention u-net: Learning where to look for the pancreas. arXiv."},{"key":"ref_38","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 International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_39","unstructured":"(2021, March 16). The Cell Tracking Challenge. Available online: http:\/\/celltrackingchallenge.net\/."},{"key":"ref_40","unstructured":"(2021, March 16). The EM Segmentation Challenge. Available online: http:\/\/brainiac2.mit.edu\/isbi_challenge\/."},{"key":"ref_41","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_42","unstructured":"(2021, March 16). The DeepGlobe Road Extraction Challenge. Available online: https:\/\/competitions.codalab.org\/competitions\/18467#learn_the_details."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhou, L., Zhang, C., and Wu, M. (2018, January 18\u201322). D-LinkNet: LinkNet With Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction. Proceedings of the CVPR Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00034"},{"key":"ref_44","unstructured":"Yu, F., and Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv."},{"key":"ref_45","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Song, H., Wang, W., Zhao, S., Shen, J., and Lam, K.M. (2018, January 8\u201314). Pyramid dilated deeper convlstm for video salient object detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_44"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., and Garcia-Rodriguez, J. (2017). A Review on Deep Learning Techniques Applied to Semantic Segmentation. arXiv.","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Shamir, R.R., Duchin, Y., Kim, J., Sapiro, G., and Harel, N. (2019). Continuous Dice Coefficient: A Method for Evaluating Probabilistic Segmentations. arXiv.","DOI":"10.1101\/306977"},{"key":"ref_51","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., and Yang, Y. (2020, January 7\u201312). Random Erasing Data Augmentation. Proceedings of the AAAI, New York, NY, USA."},{"key":"ref_52","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 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/6\/2153\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:37:59Z","timestamp":1760161079000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/6\/2153"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,19]]},"references-count":52,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["s21062153"],"URL":"https:\/\/doi.org\/10.3390\/s21062153","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,19]]}}}