{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T15:31:02Z","timestamp":1768836662157,"version":"3.49.0"},"reference-count":58,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T00:00:00Z","timestamp":1638835200000},"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":["U2034202 and 41871289"],"award-info":[{"award-number":["U2034202 and 41871289"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Youth Science and Technology Innovation Team","award":["2020JDTD0003"],"award-info":[{"award-number":["2020JDTD0003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Road extraction is important for road network renewal, intelligent transportation systems and smart cities. This paper proposes an effective method to improve road extraction accuracy and reconstruct the broken road lines caused by ground occlusion. Firstly, an attention mechanism-based convolution neural network is established to enhance feature extraction capability. By highlighting key areas and restraining interference features, the road extraction accuracy is improved. Secondly, for the common broken road problem in the extraction results, a heuristic method based on connected domain analysis is proposed to reconstruct the road. An experiment is carried out on a benchmark dataset to prove the effectiveness of this method, and the result is compared with that of several famous deep learning models including FCN8s, SegNet, U-Net and D-Linknet. The comparison shows that this model increases the IOU value and the F1 score by 3.35\u201312.8% and 2.41\u20139.8%, respectively. Additionally, the result proves the proposed method is effective at extracting roads from occluded areas.<\/jats:p>","DOI":"10.3390\/rs13244974","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T11:00:23Z","timestamp":1638874823000},"page":"4974","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Efficient Occluded Road Extraction from High-Resolution Remote Sensing Imagery"],"prefix":"10.3390","volume":"13","author":[{"given":"Dejun","family":"Feng","sequence":"first","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingyu","family":"Shen","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yakun","family":"Xie","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangge","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, Z., Feng, R., Wang, L., Zhong, Y., and Cao, L. (August, January 28). D-Resunet: Resunet and dilated convolution for high resolution satellite imagery road extraction. Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898392"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1109\/LGRS.2019.2953523","article-title":"Fully convolutional network-based ensemble method for road extraction from aerial images","volume":"17","author":"Zhang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3253","DOI":"10.1109\/TII.2018.2810291","article-title":"Intelligent transportation system in Macao based on deep self-coding learning","volume":"14","author":"Li","year":"2018","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.1109\/JSTARS.2021.3053603","article-title":"Reconstruction bias U-Net for road extraction from optical remote sensing images","volume":"14","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"114908","DOI":"10.1016\/j.eswa.2021.114908","article-title":"Integrated technique of segmentation and classifification methods with connected components analysis for road extraction from orthophoto images","volume":"176","author":"Abdollahi","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4283","DOI":"10.1109\/TITS.2019.2939536","article-title":"Corse-to-fifine road extraction based on local Dirichlet mixture models and multiscale-high order deep learning","volume":"21","author":"Chen","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s12524-019-01077-4","article-title":"Semiautomatic road extraction framework based on shape features and LS-SVM from high-resolution images","volume":"48","author":"Soni","year":"2020","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_8","first-page":"597","article-title":"Road junction identification in high resolution urban SAR images based on SVM","volume":"Volume 994","author":"Barolli","year":"2019","journal-title":"International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Shao, Z., Zhou, Z., Huang, X., and Zhang, Y. (2021). MRENet: Simultaneous extraction of road surface and road centerline in complex urban scenes from very high-resolution images. Remote Sens., 13.","DOI":"10.3390\/rs13020239"},{"key":"ref_11","first-page":"339","article-title":"Research on Extracting Road Based on Its Spectral Feature and Shape Feature","volume":"22","author":"Luo","year":"2011","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_12","unstructured":"Wang, J., Qin, Q., Yang, X., Wang, J., Ye, X., and Qin, X. (2014, January 13\u201318). Automated road extraction from multi-resolution images using spectral information and texture. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1109\/34.506793","article-title":"Automatic finding main roads in aerial image by using geometric-stochastic models and estimation","volume":"18","author":"Barzohar","year":"1996","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4144","DOI":"10.1109\/TGRS.2007.906107","article-title":"Road network extraction and intersection detection from aerial images by tracking road footprints","volume":"45","author":"Hu","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Mu, H., Zhang, Y., Li, H., Guo, Y., and Zhuang, Y. (2016, January 10\u201315). Road extraction base on Zernike algorithm on SAR image. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729323"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ghaziani, M., Mohamadi, Y., Bugra Koku, A., and Konukseven, E.I. (2013, January 24\u201326). Extraction of unstructured roads from satellite images using binary image segmentation. Proceedings of the 2013 21st Signal Processing and Communications Applications Conference, Haspolat, Turkey.","DOI":"10.1109\/SIU.2013.6531337"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ma, H., Cheng, X., Wang, X., and Yuan, J. (2013, January 16\u201318). Road information extraction from high resolution remote sensing images based on threshold segmentation and mathematical morphology. Proceedings of the 2013 6th International Congress on Image and Signal Processing, Hangzhou, China.","DOI":"10.1109\/CISP.2013.6745242"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1049\/iet-ipr.2015.0263","article-title":"Junction-aware water flow approach for urban road network extraction","volume":"10","author":"Shanmugam","year":"2016","journal-title":"IET Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2012\/417942","article-title":"Numerical solutions of a variable-order fractional financial system","volume":"2012","author":"Ma","year":"2012","journal-title":"J. Appl. Math."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sirmacek, B., and Unsalan, C. (2010, January 23\u201326). Road network extraction using edge detection and spatial voting. Proceedings of the 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.762"},{"key":"ref_22","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 2011 17th International Conference on Digital Signal Processing, Corfu, Greece.","DOI":"10.1109\/ICDSP.2011.6005015"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1007\/s12524-012-0231-6","article-title":"Road extraction using topological derivative and mathematical morphology","volume":"41","author":"Anil","year":"2013","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, Z., Liu, X., Liu, L., and Shi, J. (2018, January 26\u201327). A method of road extraction from high resolution remote image based on delaunay algorithms. Proceedings of the 2018 International Conference on Robots & Intelligent System, Changsha, China.","DOI":"10.1109\/ICRIS.2018.00040"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yager, N., and Sowmya, A. (2003, January 25\u201327). Support vector machines for road extraction from remotely sensed images. Proceedings of the Computer Analysis of Images and Patterns: 10th International Conference, Groningen, The Netherlands.","DOI":"10.1007\/978-3-540-45179-2_36"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic object-based image analysis\u2013towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","first-page":"107","article-title":"Road network extraction from high resolution multispectral satellite imagery based on object oriented techniques","volume":"2","author":"Kumar","year":"2014","journal-title":"Remote Sens. Spat. Inf. Sci."},{"key":"ref_28","unstructured":"Herumurti, D., Uchimura, K., Koutaki, G., and Uemura, T. (February, January 30). Urban road extraction based on hough transform and region growing. Proceedings of the 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, Incheon, Korea."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4772","DOI":"10.1109\/JSTARS.2014.2340394","article-title":"A new region growing-based method for road network extraction and its application on different resolution SAR Images","volume":"7","author":"Lu","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/LGRS.2014.2312000","article-title":"A semi-automatic method for road centerline extraction from VHR images","volume":"11","author":"Miao","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"2448","DOI":"10.1109\/LGRS.2015.2483680","article-title":"Multiview deep learning for land-use classification","volume":"12","author":"Luus","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Laurens, V., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","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":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards realtime object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. Comput. Sci., Available online: https:\/\/arxiv.org\/abs\/1706.05587v3."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","volume":"11211","author":"Chen","year":"2018","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","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, Part VI. Heraklion, Crete, Greece.","DOI":"10.1007\/978-3-642-15567-3_16"},{"key":"ref_42","unstructured":"Gao, J., Qi, W., and Yuan, Y. (June, January 29). Embedding structured contour and location prior in siamesed fully convolutional networks for road detection. Proceedings of the IEEE International Conference on Robotics and Automation, Singapore."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"39401","DOI":"10.1109\/ACCESS.2018.2856088","article-title":"An end-to-end neural network for road extraction from remote sensing imagery by multiple feature pyramid network","volume":"6","author":"Gao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., and Culurciello, E. (2017, January 10\u201313). Linknet: Exploiting encoder representations for effificient semantic segmentation. Proceedings of the 2017 IEEE Visual Communications and Image Processing, St. Petersburg, FL, USA.","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"ref_47","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 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00034"},{"key":"ref_48","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_49","first-page":"741","article-title":"High-resolution remote sensing image road extraction method for improving U-Net","volume":"35","author":"Wang","year":"2020","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_50","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_51","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_52","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_53","doi-asserted-by":"crossref","first-page":"014512","DOI":"10.1117\/1.JRS.15.014512","article-title":"Road extraction from satellite and aerial image using SE-Unet","volume":"15","author":"Sofla","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ren, Y., Yu, Y., and Guan, H. (2020). DA-CapsUNet: A dual-attention capsule U-Net for road extraction from remote sensing imagery. Remote Sens., 12.","DOI":"10.3390\/rs12182866"},{"key":"ref_55","first-page":"102498","article-title":"Leveraging optical and SAR data with a UU-Net for large-scale road extraction","volume":"103","author":"Lin","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","article-title":"CBAM: Convolutional block attention module","volume":"11211","author":"Woo","year":"2018","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D., and Raskar, R. (2018, January 18\u201322). Deepglobe 2018: A challenge to parse the earth through satellite images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00031"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/4974\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:42:38Z","timestamp":1760168558000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/4974"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,7]]},"references-count":58,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13244974"],"URL":"https:\/\/doi.org\/10.3390\/rs13244974","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,7]]}}}