{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T08:44:20Z","timestamp":1770453860524,"version":"3.49.0"},"reference-count":60,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T00:00:00Z","timestamp":1571961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFB2100702"],"award-info":[{"award-number":["2018YFB2100702"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41875122, 41431178, 41801351, 41671453"],"award-info":[{"award-number":["41875122, 41431178, 41801351, 41671453"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2016A030311016"],"award-info":[{"award-number":["2016A030311016"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Institute of Henan Spatio-Temporal Big Data Industrial Technology","award":["2017DJA001"],"award-info":[{"award-number":["2017DJA001"]}]},{"name":"Hunan Botong Information Co.,ltd.","award":["BTZH2018001"],"award-info":[{"award-number":["BTZH2018001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Road network extraction is one of the significant assignments for disaster emergency response, intelligent transportation systems, and real-time updating road network. Road extraction base on high-resolution remote sensing images has become a hot topic. Presently, most of the researches are based on traditional machine learning algorithms, which are complex and computational because of impervious surfaces such as roads and buildings that are discernible in the images. Given the above problems, we propose a new method to extract the road network from remote sensing images using a DenseUNet model with few parameters and robust characteristics. DenseUNet consists of dense connection units and skips connections, which strengthens the fusion of different scales by connections at various network layers. The performance of the advanced method is validated on two datasets of high-resolution images by comparison with three classical semantic segmentation methods. The experimental results show that the method can be used for road extraction in complex scenes.<\/jats:p>","DOI":"10.3390\/rs11212499","type":"journal-article","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T11:05:18Z","timestamp":1572001518000},"page":"2499","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":106,"title":["Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet"],"prefix":"10.3390","volume":"11","author":[{"given":"Jiang","family":"Xin","sequence":"first","affiliation":[{"name":"Department of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China"}]},{"given":"Xinchang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China"}]},{"given":"Zhiqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}]},{"given":"Wu","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhang, X., Sun, Y., and Zhang, P. (2018). Road Centerline Extraction from Very-High-Resolution Aerial Image and LiDAR Data Based on Road Connectivity. Remote Sens., 10.","DOI":"10.3390\/rs10081284"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhou, T., Sun, C., and Fu, H. (2019). Road Information Extraction from High-Resolution Remote Sensing Images Based on Road Reconstruction. Remote Sens., 11.","DOI":"10.3390\/rs11010079"},{"key":"ref_3","first-page":"209","article-title":"Automatic Road Network Recognition and Extraction for Urban Planning","volume":"5","author":"Bong","year":"2009","journal-title":"Int. J. Appl. Sci. Eng. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/LGRS.2012.2214761","article-title":"Road centerline extraction from high-resolution imagery based on shape features and multivariate adaptive regression splines","volume":"10","author":"Miao","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1080\/13658810210149416","article-title":"Quantitative measures for spatial information of maps","volume":"16","author":"Li","year":"2002","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, B., Wu, H., Wang, Y., and Liu, W. (2015). Main road extraction from zy-3 grayscale imagery based on directional mathematical morphology and vgi prior knowledge in urban areas. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0138071"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s13640-015-0062-9","article-title":"Connected component-based technique for automatic extraction of road centerline in high resolution satellite images","volume":"2015","author":"Sujatha","year":"2015","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1080\/01431160802546837","article-title":"Road centreline extraction from high\u2014Resolution imagery based on multiscale structural features and support vector machines","volume":"30","author":"Huang","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Daniilidis, K., Maragos, P., and Paragios, N. (2010, January 5\u201311). Learning to detect roads in high-resolution aerial images. Proceedings of the European Conference on Computer Vision (ECCV), Crete, Greece.","DOI":"10.1007\/978-3-642-15561-1"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Cheng, G., Wang, Y., and Gong, Y. (2014, January 27\u201330). Urban road extraction via graph cuts based probability propagation. Proceedings of the IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7026027"},{"key":"ref_12","first-page":"1","article-title":"Multiple object extraction from aerial imagery with convolutional neural networks","volume":"2016","author":"Saito","year":"2016","journal-title":"J. Electron. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.isprsjprs.2017.02.008","article-title":"Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images","volume":"126","author":"Alshehhi","year":"2017","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.14358\/PERS.70.12.1365","article-title":"Road extraction using SVM and image segmentation","volume":"70","author":"Song","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_16","first-page":"1978","article-title":"Road Extraction from High-spatial-resolution Remote Sensing Image by Combining with GVF Snake with Salient Features","volume":"46","author":"Wang","year":"2017","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1142\/S0218001400000635","article-title":"Detection of roads from satellite images using optimal search","volume":"14","author":"Rianto","year":"2000","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_18","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_19","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_20","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_21","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_22","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). Imagenet classification with deep convolutional neural networks. Proceedings of the Twenty-Sixth Annual Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","article-title":"Learning hierarchical features for scene labeling","volume":"35","author":"Farabet","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 11\u201318). Learning deconvolution network for semantic segmentation. Proceedings of the IEEE international conference on computer vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, P., Zang, Y., and Wang, C. (2016, January 10\u201315). Road network extraction via deep learning and line integral convolution. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729408"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv.","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref_27","unstructured":"Llu\u00eds, M., Chris, C.-B., and Jian, S. (2015, January 17\u201321). Document modeling with gated recurrent neural network for sentiment classification. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), Lisbon, Portugal."},{"key":"ref_28","unstructured":"Mnih, V. (2013). Machine Learning for Aerial Image Labeling. [Ph.D. Thesis, University of Toronto]."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, H., Yang, D., Wang, S., Wang, S.Y., and Li, Y.F. (2019). Road Extraction by Using Atrous Spatial Pyramid Pooling Integrated Encoder-Decoder Network and Structural Similarity Loss. Remote Sens., 11.","DOI":"10.3390\/rs11091015"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, Z., and Wang, Y. (2019). JointNet: A Common Neural Network for Road and Building Extraction. Remote Sens., 11.","DOI":"10.3390\/rs11060696"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1080\/2150704X.2018.1557791","article-title":"A Y-Net deep learning method for road segmentation using high-resolution visible remote sensing images","volume":"10","author":"Li","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1109\/LGRS.2017.2672734","article-title":"Road structure refined CNN for road extraction in aerial image","volume":"14","author":"Wei","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","first-page":"207","article-title":"U-Net based semantic segmentation method for high resolution remote sensing image","volume":"55","author":"Su","year":"2019","journal-title":"Comput. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, X., Han, X., Li, C., Tang, X., Zhou, H., and Jiao, L. (2019). Aerial Image Road Extraction Based on an Improved Generative Adversarial Network. Remote Sens., 11.","DOI":"10.3390\/rs11080930"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Gao, L., Song, W., Dai, J., and Chen, Y. (2019). Road Extraction from High-Resolution Remote Sensing Imagery Using Refined Deep Residual Convolutional Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11050552"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7209","DOI":"10.1109\/TGRS.2019.2912301","article-title":"Road Detection and Centerline Extraction Via Deep Recurrent Convolutional Neural Network U-Net","volume":"59","author":"Yang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","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_38","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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00034"},{"key":"ref_39","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\u201322). Roadtracer: Automatic extraction of road networks from aerial images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00496"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2279","DOI":"10.1109\/JSTARS.2019.2909478","article-title":"Road Segmentation of Unmanned Aerial Vehicle Remote Sensing Images Using Adversarial Network with Multiscale Context Aggregation","volume":"12","author":"Li","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Xu, Y., Xie, Z., Feng, Y., and Chen, Z.L. (2018). Road extraction from high-resolution remote sensing imagery using deep learning. Remote Sens., 10.","DOI":"10.3390\/rs10091461"},{"key":"ref_42","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 (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_43","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 (MICCAI), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","unstructured":"Normalization, B. (2015). Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat. Med."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1109\/72.977323","article-title":"A general backpropagation algorithm for feedforward neural networks learning","volume":"13","author":"Yu","year":"2002","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., and Ren, S. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Van, D.M. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer vVision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_51","unstructured":"Sermanet, P., Chintala, S., and LeCun, Y. (2012). Convolutional neural networks applied to house numbers digit classification. arXiv."},{"key":"ref_52","unstructured":"LeCun, Y., Boser, B.E., and Denker, J.S. (1990). Handwritten Digit Recognition with a Back-Propagation Network. Advances in Neural Information Processing Systems 2, Morgan Kaufmann Publishers."},{"key":"ref_53","unstructured":"Simard, P.Y., Steinkraus, D., and Platt, J.C. (2003, January 3\u20136). Best practices for convolutional neural networks applied to visual document analysis. Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), Edinburgh, Scotland."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique. J","volume":"16","author":"Chawla","year":"2002","journal-title":"Artif. Intell. Res."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Weinzaepfel, P., Revaud, J., and Harchaoui, Z. (2013, January 23\u201328). DeepFlow: Large displacement optical flow with deep matching. Proceedings of the IEEE International Conference on Computer Vision (CVPR), Portland, OR, USA.","DOI":"10.1109\/ICCV.2013.175"},{"key":"ref_56","unstructured":"(2019, October 24). Available online: https:\/\/www.cs.toronto.edu\/~vmnih\/data\/."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X.G., and Jia, J.Y. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_58","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_59","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C.X., Yu, G., and Song, N. (2018, January 8\u201314). Bisenet: Bilateral segmentation network for real-time semantic segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"ref_60","unstructured":"Strobl, J., Blaschke, T., and Griesbner, G. (2000). Multiresolution segmentation: An optimization approach for high quality multi-scale image segmentation. Angewandte Geographische Information\u2014Sverarbeitung, Wichmann Verlag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/21\/2499\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:29:23Z","timestamp":1760189363000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/21\/2499"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,25]]},"references-count":60,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["rs11212499"],"URL":"https:\/\/doi.org\/10.3390\/rs11212499","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,25]]}}}