Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91288
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorLiang, P-
dc.creatorShi, W:rp00069-
dc.creatorDing, Y-
dc.creatorLiu, Z-
dc.creatorShang, H-
dc.date.accessioned2021-11-02T08:22:04Z-
dc.date.available2021-11-02T08:22:04Z-
dc.identifier.urihttp://hdl.handle.net/10397/91288-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Liang, P.; Shi,W.; Ding, Y.;Liu, Z.; Shang, H. Road Extraction from High Resolution Remote Sensing Images Based on Vector Field Learning. Sensors 2021, 21, 3152 is available at https://doi.org/10.3390/s21093152en_US
dc.subjectDCNNen_US
dc.subjectEncoder-decoderen_US
dc.subjectHigh resolution remote sensing imageen_US
dc.subjectRoad extractionen_US
dc.subjectVector field learningen_US
dc.titleRoad extraction from high resolution remote sensing images based on vector field learningen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume21-
dc.identifier.issue9-
dc.identifier.doi10.3390/s21093152-
dcterms.abstractAccurate and up-to-date road network information is very important for the Geographic Information System (GIS) database, traffic management and planning, automatic vehicle navigation, emergency response and urban pollution sources investigation. In this paper, we use vector field learning to extract roads from high resolution remote sensing imaging. This method is usually used for skeleton extraction in nature image, but seldom used in road extraction. In order to improve the accuracy of road extraction, three vector fields are constructed and combined respectively with the normal road mask learning by a two-task network. The results show that all the vector fields are able to significantly improve the accuracy of road extraction, no matter the field is constructed in the road area or completely outside the road. The highest F1 score is 0.7618, increased by 0.053 compared with using only mask learning.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationSensors, May 2021, v. 21, no. 9, 3152-
dcterms.isPartOfSensors-
dcterms.issued2021-05-
dc.identifier.scopus2-s2.0-85105017673-
dc.identifier.eissn1424-8220-
dc.identifier.artn3152-
dc.description.validate202110 bcvc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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