{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:33:22Z","timestamp":1760402002746,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T00:00:00Z","timestamp":1617753600000},"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":["42071428","420713743"],"award-info":[{"award-number":["42071428","420713743"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Liaoning Provincial Department of Education Project Services Local Project under Grant","award":["LJ2019FL008"],"award-info":[{"award-number":["LJ2019FL008"]}]},{"name":"Beijing Key Laboratory of Urban Spatial Information Engineering","award":["2020221"],"award-info":[{"award-number":["2020221"]}]},{"name":"Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of Ministry of Natural Resources","award":["2020-3-5"],"award-info":[{"award-number":["2020-3-5"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Road extraction in rural areas is one of the most fundamental tasks in the practical application of remote sensing. In recent years, sample-driven methods have achieved state-of-the-art performance in road extraction tasks. However, sample-driven methods are prohibitively expensive and laborious, especially when dealing with rural roads with irregular curvature changes, narrow widths, and diverse materials. The template matching method can overcome these difficulties to some extent and achieve impressive road extraction results. This method also has the advantage of the vectorization of road extraction results, but the automation is limited. Straight line sequences can be substituted for curves, and the use of the color space can increase the recognition of roads and nonroads. A model-driven-to-sample-driven road extraction method for rural areas with a much higher degree of automation than existing template matching methods is proposed in this study. Without prior samples, on the basis of the geometric characteristics of narrow and long roads and using the advantages of straight lines instead of curved lines, the road center point extraction model is established through length constraints and gray mean contrast constraints of line sequences, and the extraction of some rural roads is completed through topological connection analysis. In addition, we take the extracted road center point and manual input data as local samples, use the improved line segment histogram to determine the local road direction, and use the panchromatic and hue, saturation, value (HSV) space interactive matching model as the matching measure to complete the road tracking extraction. Experimental results show that, for different types of data and scenarios on the premise, the accuracy and recall rate of the evaluation indicators reach more than 98%, and, compared with other methods, the automation of this algorithm has increased by more than 40%.<\/jats:p>","DOI":"10.3390\/rs13081417","type":"journal-article","created":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T11:31:59Z","timestamp":1617795119000},"page":"1417","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Model-Driven-to-Sample-Driven Method for Rural Road Extraction"],"prefix":"10.3390","volume":"13","author":[{"given":"Jiguang","family":"Dai","sequence":"first","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 12300, China"},{"name":"Institute of Spatiotemporal Transportation Data, Liaoning Technical University, Fuxin 12300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3591-6807","authenticated-orcid":false,"given":"Rongchen","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 12300, China"},{"name":"Institute of Spatiotemporal Transportation Data, Liaoning Technical University, Fuxin 12300, China"}]},{"given":"Litao","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 12300, China"},{"name":"Institute of Spatiotemporal Transportation Data, Liaoning Technical University, Fuxin 12300, China"}]},{"given":"Zimo","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 12300, China"},{"name":"Institute of Spatiotemporal Transportation Data, Liaoning Technical University, Fuxin 12300, China"}]},{"given":"Jialin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 12300, China"},{"name":"Institute of Spatiotemporal Transportation Data, Liaoning Technical University, Fuxin 12300, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1080\/10106049.2014.911967","article-title":"GIS-based sustainable city compactness assessment using integration of MCDM, Bayes theorem and RADAR technology","volume":"30","author":"Abdullahi","year":"2015","journal-title":"Geocarto Int."},{"key":"ref_2","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_3","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_4","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/1476-072X-8-24","article-title":"The importance of accurate road data for spatial applications in public health: Customizing a road network","volume":"8","author":"Frizzelle","year":"2009","journal-title":"Int. J. Health Geogr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"232","DOI":"10.4314\/ejesm.v5i3.3","article-title":"Impact of Road Transport on Agricultural Development: A Nigerian Example","volume":"5","author":"Tunde","year":"2012","journal-title":"Ethiop. J. Environ. Stud. Manag."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Zhang, W., Tang, P., Corpetti, T., and Zhao, L. (2021). WTS: A Weakly towards Strongly Supervised Learning Framework for Remote Sensing Land Cover Classification Using Segmentation Models. Remote Sens., 13.","DOI":"10.3390\/rs13030394"},{"key":"ref_8","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_9","first-page":"56","article-title":"A method for road extraction from remote sensing imagery","volume":"27","author":"Li","year":"2015","journal-title":"Remote Sens. Land Resour."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1080\/01431161.2016.1264026","article-title":"Object-based road extraction from satellite images using antcolony optimization","volume":"38","author":"Maboudi","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1111\/phor.12123","article-title":"Road vectorisation from high-resolution imagery based on dynamic clustering using particle swarm optimisation","volume":"30","author":"Ameri","year":"2015","journal-title":"Photogramm. Rec."},{"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":"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_15","doi-asserted-by":"crossref","unstructured":"Maboudi, M., Amini, J., Hahn, M., and Saati, M. (2016). Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting. Remote Sens., 8.","DOI":"10.3390\/rs8080637"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1080\/2150704X.2017.1422873","article-title":"An object-based supervised classification framework for very-high-resolution remote sensing images using convolutional neural networks","volume":"9","author":"Zhang","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rse.2010.12.017","article-title":"Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery","volume":"115","author":"Myint","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_18","first-page":"105","article-title":"A divided and stratified extraction method of high-resolution remote sensing information for cropland in hilly and mountainous areas based on deep learning","volume":"50","author":"Liu","year":"2021","journal-title":"Acta Geod. Cartogr."},{"key":"ref_19","first-page":"1","article-title":"Generalized photogrammetry of spaceborne, airborne and terrestrial multi-source remote sensing datasets","volume":"50","author":"Zhang","year":"2021","journal-title":"Acta Geod. Cartogr."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1061\/(ASCE)0887-3801(2000)14:1(60)","article-title":"Automatic road detection in grayscale aerial images","volume":"14","author":"Treash","year":"2000","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_21","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":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/JSTARS.2010.2094181","article-title":"Application of a Fast Linear Feature Detector to Road Extraction From Remotely Sensed Imagery","volume":"4","author":"Shao","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","first-page":"28","article-title":"Road extraction method based on path morphology for high resolution remote sensing imagery","volume":"34","author":"Dai","year":"2019","journal-title":"Remote Sens. Inf."},{"key":"ref_24","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 European Conference on Computer Vision, Heraklion, Crete, Greece.","DOI":"10.1007\/978-3-642-15567-3_16"},{"key":"ref_25","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":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W., and Frangi, A. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-24571-3"},{"key":"ref_27","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_28","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_29","doi-asserted-by":"crossref","unstructured":"Panboonyuen, T., Jitkajornwanich, K., Lawawirojwong, S., Srestasathiern, P., and Vateekul, P. (2017). Road Segmentation of Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields. Remote Sens., 9.","DOI":"10.20944\/preprints201706.0012.v3"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.isprsjprs.2020.08.019","article-title":"BT-RoadNet: A boundary and topologically-aware neural network forroad extraction from high-resolution remote sensing imagery","volume":"168","author":"Zhou","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, H., Yang, D., Wang, S., Wang, S., and Li, Y. (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_34","first-page":"611","article-title":"Feature-representation-transfer based road extraction method for cross-domain aerial images","volume":"49","author":"Wang","year":"2020","journal-title":"Acta Geod. Cartogr."},{"key":"ref_35","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_36","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_37","first-page":"155","article-title":"Semi automatic extraction of linear features based on rectangular template matching","volume":"37","author":"Sun","year":"2015","journal-title":"J. Southwest."},{"key":"ref_38","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_39","first-page":"950","article-title":"Road extraction from high-resolution remote sensing images based on adaptive circular template and saliency map","volume":"47","author":"Lian","year":"2018","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Dai, J., Zhu, T., Zhang, Y., Ma, R., and Li, W. (2019). Lane-Level Road Extraction from High-Resolution Optical Satellite Images. Remote Sens., 11.","DOI":"10.3390\/rs11222672"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1109\/JSTARS.2019.2955277","article-title":"Road Extraction From High-Resolution Satellite Images Based on Multiple Descriptors","volume":"13","author":"Dai","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","first-page":"117096","DOI":"10.1109\/ACCESS.2020.3004968","article-title":"Morphological Attribute Profile Cube and Deep Random Forest for Small Sample Classification of Hyperspectral Image","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_44","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_45","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Xiong, Z., Zang, Y., Wang, C., Li, J., and Li, X. (2019). Topology-Aware Road Network Extraction via Multi-Supervised Generative Adversarial Networks. Remote Sens., 11.","DOI":"10.3390\/rs11091017"},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"9014","DOI":"10.3390\/rs6099014","article-title":"Automatic Road Centerline Extraction from Imagery Using Road GPS Data","volume":"6","author":"Cao","year":"2014","journal-title":"Remote Sens."},{"key":"ref_48","first-page":"1","article-title":"Image smoothing via L 0 gradient minimization","volume":"30","author":"Xu","year":"2011","journal-title":"ACM Trans. Graph."},{"key":"ref_49","first-page":"218","article-title":"A line extraction method for chain code tracking with phase verification","volume":"46","author":"Dai","year":"2017","journal-title":"Acta Geod. Geophys."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ma, J.-Q. (2009, January 7\u20138). Content-Based Image Retrieval with HSV Color Space and Texture Features. Proceedings of the 2009 International Conference on Web Information Systems and Mining, Shanghai, China. Institute of Electrical and Electronics Engineers (IEEE).","DOI":"10.1109\/WISM.2009.20"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1016\/j.patrec.2011.06.001","article-title":"EDLines: A real-time line segment detector with a false detection control","volume":"32","author":"Akinlar","year":"2011","journal-title":"Pattern Recognit. Lett."},{"key":"ref_52","first-page":"804","article-title":"Development and prospect of road extraction method for optical remote sensing image","volume":"24","author":"Dai","year":"2020","journal-title":"J. Remote Sens."},{"key":"ref_53","unstructured":"Vosselman, G., and de Knech, J. (1995). Automatic Extraction of Man-Made Objects from Aerial and Space Images, Birkhauser Verlag."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TPAMI.2008.300","article-title":"LSD: A Fast Line Segment Detector with a False Detection Control","volume":"32","author":"Jakubowicz","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_55","unstructured":"(2017, November 01). Land Use Status Classification. GB\/T 21010\u20132017. Available online: https:\/\/www.chinesestandard.net\/PDF\/English.aspx\/GBT21010-2017."},{"key":"ref_56","first-page":"281","article-title":"Adaptive window local matching algorithm based on hsv color space","volume":"55","author":"Su","year":"2018","journal-title":"Laser Optoelectron. Prog."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1417\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:26:29Z","timestamp":1760361989000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1417"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,7]]},"references-count":56,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13081417"],"URL":"https:\/\/doi.org\/10.3390\/rs13081417","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,4,7]]}}}