Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet
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...
Saved in:
| Published in | Remote sensing (Basel, Switzerland) Vol. 11; no. 21; p. 2499 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
25.10.2019
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | 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. |
|---|---|
| AbstractList | 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. |
| Author | Xin, Jiang Fang, Wu Zhang, Zhiqiang Zhang, Xinchang |
| Author_xml | – sequence: 1 givenname: Jiang surname: Xin fullname: Xin, Jiang – sequence: 2 givenname: Xinchang surname: Zhang fullname: Zhang, Xinchang – sequence: 3 givenname: Zhiqiang surname: Zhang fullname: Zhang, Zhiqiang – sequence: 4 givenname: Wu surname: Fang fullname: Fang, Wu |
| BookMark | eNptkUtLLDEQhYMo-Nz4CxrciNDXymu6sxQf1wFRGHUd0unKmKG7o0mP6L8341y8ItYmVcWXw0nOLtkcwoCEHFL4w7mC05goZZQJpTbIDoOKlYIptvmt3yYHKS0gF-dUgdghd7Ng2uLybYzGjj4MRXDFtZ8_lTNMoVt-rmbYhxGLexySH-bFtDdzTMUFRv-KbeFi6PMwJHy8xXGfbDnTJTz4d-6Rx6vLh_Pr8ubu7_T87Ka0YgJjWUlqHZdWOVZL1zTOQmO4qexEoeA1FTLbc05UXLUUBDKEClVdo1Mwabnke2S61m2DWejn6HsT33UwXn8uQpxrE0dvO9SSNyhxQqECKqw0BhxlpgGlGiErWWet47XWcwwvS0yj7n2y2HVmwLBMmqlaqowzltGjH-giLOOQX6qZlMBqqOlK8GRN2RhSiui-DFLQq6j0_6gyDD9g60ez-vicie9-u_IBPP2VOQ |
| CitedBy_id | crossref_primary_10_1061_JITSE4_ISENG_2351 crossref_primary_10_1109_LGRS_2021_3065148 crossref_primary_10_3390_s21093152 crossref_primary_10_3390_app12084068 crossref_primary_10_3390_rs16050839 crossref_primary_10_48084_etasr_5247 crossref_primary_10_1109_JSTARS_2021_3071353 crossref_primary_10_3390_rs14215342 crossref_primary_10_3390_s24051708 crossref_primary_10_1016_j_jag_2022_102833 crossref_primary_10_1109_TGRS_2022_3197546 crossref_primary_10_1109_ACCESS_2023_3253289 crossref_primary_10_1007_s42461_022_00548_6 crossref_primary_10_1109_JMASS_2023_3299330 crossref_primary_10_3390_rs16122056 crossref_primary_10_1109_MGRS_2024_3491014 crossref_primary_10_1109_LGRS_2020_3028468 crossref_primary_10_54097_ijeh_v4i3_1649 crossref_primary_10_3390_rs12182866 crossref_primary_10_47164_ijngc_v14i3_1301 crossref_primary_10_1016_j_isprsjprs_2023_03_012 crossref_primary_10_1109_LGRS_2021_3093101 crossref_primary_10_1016_j_isprsjprs_2022_09_005 crossref_primary_10_3390_s22207735 crossref_primary_10_1002_pat_6506 crossref_primary_10_1016_j_neunet_2023_01_031 crossref_primary_10_3390_rs13010158 crossref_primary_10_1007_s11042_022_12994_x crossref_primary_10_3390_rs15153799 crossref_primary_10_1080_10106049_2022_2060319 crossref_primary_10_1016_j_dsp_2024_104637 crossref_primary_10_3390_ijgi10050329 crossref_primary_10_3390_rs14236015 crossref_primary_10_1109_TGRS_2023_3305031 crossref_primary_10_1109_JSTARS_2021_3083055 crossref_primary_10_3390_rs14194872 crossref_primary_10_1080_10106049_2021_1926555 crossref_primary_10_3389_fpls_2023_1103276 crossref_primary_10_1371_journal_pone_0297152 crossref_primary_10_1016_j_iswcr_2021_10_004 crossref_primary_10_3390_su152316537 crossref_primary_10_6002_ect_2021_0480 crossref_primary_10_3390_jimaging8050124 crossref_primary_10_3390_rs12223764 crossref_primary_10_3390_rs13183710 crossref_primary_10_3390_app15031027 crossref_primary_10_3390_rs13244958 crossref_primary_10_1016_j_isprsjprs_2020_08_019 crossref_primary_10_3390_rs14020349 crossref_primary_10_3390_rs12091444 crossref_primary_10_1007_s10553_025_01825_y crossref_primary_10_1049_ipr2_12948 crossref_primary_10_1109_ACCESS_2020_3027573 crossref_primary_10_3390_rs13030465 crossref_primary_10_1007_s41064_022_00194_z crossref_primary_10_1016_j_procs_2024_04_158 crossref_primary_10_1109_TGRS_2020_3034011 crossref_primary_10_1109_TGRS_2024_3379988 crossref_primary_10_52547_jgit_9_3_109 crossref_primary_10_3390_ijgi11100502 crossref_primary_10_1080_17480930_2021_1949800 crossref_primary_10_3390_rs15174215 crossref_primary_10_1016_j_isprsjprs_2023_07_026 crossref_primary_10_3390_rs14030613 crossref_primary_10_1007_s44230_023_00015_5 crossref_primary_10_3390_rs15082099 crossref_primary_10_1016_j_isprsjprs_2022_10_005 crossref_primary_10_1088_1755_1315_1127_1_012047 crossref_primary_10_1186_s10033_024_01018_4 crossref_primary_10_3390_ijgi11010009 crossref_primary_10_3390_math10111915 crossref_primary_10_1016_j_jksuci_2024_102009 crossref_primary_10_1080_14498596_2024_2309605 crossref_primary_10_1080_07038992_2021_1929884 crossref_primary_10_35784_acs_2023_19 crossref_primary_10_1007_s12524_022_01532_9 crossref_primary_10_1109_TAI_2024_3357437 crossref_primary_10_1109_JSTARS_2024_3486723 crossref_primary_10_3390_rs13214235 crossref_primary_10_1007_s13369_022_07082_z crossref_primary_10_1016_j_rsase_2025_101465 |
| Cites_doi | 10.3390/rs11060696 10.1007/978-3-319-24574-4_28 10.1109/CVPRW.2018.00034 10.1109/LGRS.2017.2672734 10.1109/TGRS.2010.2041783 10.1371/journal.pone.0138071 10.1109/72.977323 10.1109/TGRS.2012.2190078 10.1016/j.isprsjprs.2017.02.008 10.1109/LGRS.2012.2214761 10.1038/s41591-018-0316-z 10.1109/CVPR.2017.660 10.1109/LGRS.2006.873875 10.3390/rs10091461 10.1109/ICIP.2014.7026027 10.1109/TGRS.2017.2669341 10.1109/JSTARS.2019.2909478 10.1142/S0218001400000635 10.1080/2150704X.2018.1557791 10.1186/s13640-015-0062-9 10.3390/rs11010079 10.1007/978-3-642-15561-1 10.1109/TPAMI.2016.2644615 10.14358/PERS.70.12.1365 10.1109/CVPR.2018.00496 10.3390/rs10081284 10.3390/rs11050552 10.1038/nature14539 10.1613/jair.953 10.1080/01431161.2010.540587 10.1080/13658810210149416 10.1109/ICCV.2015.178 10.1080/01431160802546837 10.1109/TPAMI.2012.231 10.1109/IGARSS.2016.7729408 10.1109/TGRS.2019.2912301 10.1109/CVPR.2016.90 10.1109/CVPR.2015.7298965 10.1109/CVPR.2017.243 10.1109/ICCV.2013.175 10.3390/rs11080930 10.1007/978-3-030-01234-2_49 10.3115/v1/D14-1181 10.3390/rs11091015 10.1007/978-3-030-01261-8_20 10.1109/LGRS.2018.2802944 |
| ContentType | Journal Article |
| Copyright | 2019 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 (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2019 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 (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD 8FE 8FG ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ BHPHI BKSAR C1K CCPQU DWQXO F28 FR3 H8D H8G HCIFZ JG9 JQ2 KR7 L6V L7M L~C L~D M7S P5Z P62 P64 PCBAR PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS 7S9 L.6 DOA |
| DOI | 10.3390/rs11212499 |
| DatabaseName | CrossRef Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Ecology Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Aerospace Collection ProQuest Central Essentials Proquest Central Technology Collection Natural Science Collection Earth, Atmospheric & Aquatic Science Collection Environmental Sciences and Pollution Management ProQuest One ProQuest Central ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Copper Technical Reference Library SciTech Premium Collection Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Earth, Atmospheric & Aquatic Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection AGRICOLA AGRICOLA - Academic DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database Materials Research Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China Materials Business File Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences Engineered Materials Abstracts Natural Science Collection Chemoreception Abstracts ProQuest Central (New) Engineering Collection ANTE: Abstracts in New Technology & Engineering Advanced Technologies & Aerospace Collection Engineering Database Aluminium Industry Abstracts ProQuest One Academic Eastern Edition Electronics & Communications Abstracts Earth, Atmospheric & Aquatic Science Database ProQuest Technology Collection Ceramic Abstracts Ecology Abstracts Biotechnology and BioEngineering Abstracts ProQuest One Academic UKI Edition Solid State and Superconductivity Abstracts Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Central (Alumni Edition) ProQuest One Community College Earth, Atmospheric & Aquatic Science Collection ProQuest Central Aerospace Database Copper Technical Reference Library ProQuest Engineering Collection Biotechnology Research Abstracts ProQuest Central Korea Advanced Technologies Database with Aerospace Civil Engineering Abstracts ProQuest SciTech Collection METADEX Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database Materials Science & Engineering Collection Corrosion Abstracts AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | CrossRef Publicly Available Content Database AGRICOLA |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Geography |
| EISSN | 2072-4292 |
| ExternalDocumentID | oai_doaj_org_article_53be5e6107014c5aa0f12ab099b45758 10_3390_rs11212499 |
| GroupedDBID | 29P 2WC 5VS 8FE 8FG 8FH AADQD AAHBH AAYXX ABDBF ABJCF ACUHS ADBBV ADMLS AENEX AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS BCNDV BENPR BGLVJ BHPHI BKSAR CCPQU CITATION E3Z ESX FRP GROUPED_DOAJ HCIFZ I-F KQ8 L6V LK5 M7R M7S MODMG M~E OK1 P62 PCBAR PHGZM PHGZT PIMPY PROAC PTHSS TR2 TUS 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD ABUWG AZQEC C1K DWQXO F28 FR3 H8D H8G JG9 JQ2 KR7 L7M L~C L~D P64 PKEHL PQEST PQGLB PQQKQ PQUKI PRINS 7S9 L.6 PUEGO |
| ID | FETCH-LOGICAL-c460t-751cf35c9f285fbbfc0ba3a7c69e438145190ff4739d104e2e07e988ef906d353 |
| IEDL.DBID | DOA |
| ISSN | 2072-4292 |
| IngestDate | Wed Aug 27 01:29:59 EDT 2025 Fri Jul 11 01:19:45 EDT 2025 Fri Jul 25 09:29:34 EDT 2025 Tue Jul 01 04:14:53 EDT 2025 Thu Apr 24 23:04:03 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 21 |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c460t-751cf35c9f285fbbfc0ba3a7c69e438145190ff4739d104e2e07e988ef906d353 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://doaj.org/article/53be5e6107014c5aa0f12ab099b45758 |
| PQID | 2550280818 |
| PQPubID | 2032338 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_53be5e6107014c5aa0f12ab099b45758 proquest_miscellaneous_2985909922 proquest_journals_2550280818 crossref_primary_10_3390_rs11212499 crossref_citationtrail_10_3390_rs11212499 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20191025 |
| PublicationDateYYYYMMDD | 2019-10-25 |
| PublicationDate_xml | – month: 10 year: 2019 text: 20191025 day: 25 |
| PublicationDecade | 2010 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Remote sensing (Basel, Switzerland) |
| PublicationYear | 2019 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | ref_50 Miao (ref_4) 2012; 10 Cheng (ref_14) 2017; 55 ref_58 ref_57 ref_56 ref_11 ref_55 ref_53 ref_52 ref_51 Huang (ref_8) 2009; 30 ref_59 Li (ref_5) 2002; 16 ref_60 Bong (ref_3) 2009; 5 Li (ref_31) 2019; 10 ref_25 Rianto (ref_17) 2000; 14 ref_24 ref_22 Chawla (ref_54) 2002; 16 Li (ref_40) 2019; 12 ref_29 ref_28 ref_27 Wei (ref_32) 2017; 14 Yu (ref_48) 2002; 13 ref_26 Badrinarayanan (ref_44) 2017; 39 Saito (ref_12) 2016; 2016 Wang (ref_16) 2017; 46 Zhang (ref_18) 2011; 32 Song (ref_15) 2004; 70 Alshehhi (ref_13) 2017; 126 Esteva (ref_47) 2019; 25 ref_35 ref_34 Unsalan (ref_10) 2012; 50 ref_30 Su (ref_33) 2019; 55 Gamba (ref_20) 2006; 3 ref_39 Movaghati (ref_19) 2010; 48 Li (ref_21) 2016; 44 ref_38 Yang (ref_36) 2019; 59 ref_45 Zhang (ref_37) 2018; 15 ref_43 ref_42 ref_41 ref_1 ref_2 LeCun (ref_46) 2015; 521 ref_49 ref_9 Sujatha (ref_7) 2015; 2015 ref_6 Farabet (ref_23) 2012; 35 |
| References_xml | – ident: ref_30 doi: 10.3390/rs11060696 – ident: ref_43 doi: 10.1007/978-3-319-24574-4_28 – ident: ref_38 doi: 10.1109/CVPRW.2018.00034 – volume: 14 start-page: 709 year: 2017 ident: ref_32 article-title: Road structure refined CNN for road extraction in aerial image publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2017.2672734 – ident: ref_51 – volume: 48 start-page: 2807 year: 2010 ident: ref_19 article-title: Road extraction from satellite images using particle filtering and extended Kalman filtering publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2010.2041783 – ident: ref_6 doi: 10.1371/journal.pone.0138071 – volume: 13 start-page: 251 year: 2002 ident: ref_48 article-title: A general backpropagation algorithm for feedforward neural networks learning publication-title: IEEE Trans. Neural Netw. doi: 10.1109/72.977323 – volume: 50 start-page: 4441 year: 2012 ident: ref_10 article-title: Road network detection using probabilistic and graph theoretical methods publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2012.2190078 – volume: 126 start-page: 245 year: 2017 ident: ref_13 article-title: Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images publication-title: ISPRS-J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2017.02.008 – volume: 10 start-page: 583 year: 2012 ident: ref_4 article-title: Road centerline extraction from high-resolution imagery based on shape features and multivariate adaptive regression splines publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2012.2214761 – volume: 25 start-page: 24 year: 2019 ident: ref_47 article-title: A guide to deep learning in healthcare publication-title: Nat. Med. doi: 10.1038/s41591-018-0316-z – ident: ref_57 doi: 10.1109/CVPR.2017.660 – volume: 3 start-page: 387 year: 2006 ident: ref_20 article-title: Improving urban road extraction in high-resolution images exploiting directional filtering, perceptual grouping, and simple topological concepts publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2006.873875 – ident: ref_41 doi: 10.3390/rs10091461 – ident: ref_56 – ident: ref_27 – ident: ref_52 – ident: ref_11 doi: 10.1109/ICIP.2014.7026027 – volume: 55 start-page: 3322 year: 2017 ident: ref_14 article-title: Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2017.2669341 – volume: 12 start-page: 2279 year: 2019 ident: ref_40 article-title: Road Segmentation of Unmanned Aerial Vehicle Remote Sensing Images Using Adversarial Network with Multiscale Context Aggregation publication-title: IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. doi: 10.1109/JSTARS.2019.2909478 – volume: 55 start-page: 207 year: 2019 ident: ref_33 article-title: U-Net based semantic segmentation method for high resolution remote sensing image publication-title: Comput. Appl. – volume: 14 start-page: 1009 year: 2000 ident: ref_17 article-title: Detection of roads from satellite images using optimal search publication-title: Int. J. Pattern Recognit. Artif. Intell. doi: 10.1142/S0218001400000635 – volume: 10 start-page: 381 year: 2019 ident: ref_31 article-title: A Y-Net deep learning method for road segmentation using high-resolution visible remote sensing images publication-title: Remote Sens. Lett. doi: 10.1080/2150704X.2018.1557791 – volume: 2015 start-page: 8 year: 2015 ident: ref_7 article-title: Connected component-based technique for automatic extraction of road centerline in high resolution satellite images publication-title: EURASIP J. Image Video Process. doi: 10.1186/s13640-015-0062-9 – ident: ref_2 doi: 10.3390/rs11010079 – ident: ref_9 doi: 10.1007/978-3-642-15561-1 – volume: 39 start-page: 2481 year: 2017 ident: ref_44 article-title: Segnet: A deep convolutional encoder-decoder architecture for image segmentation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2016.2644615 – ident: ref_45 – volume: 70 start-page: 1365 year: 2004 ident: ref_15 article-title: Road extraction using SVM and image segmentation publication-title: Photogramm. Eng. Remote Sens. doi: 10.14358/PERS.70.12.1365 – ident: ref_28 – ident: ref_39 doi: 10.1109/CVPR.2018.00496 – ident: ref_53 – ident: ref_1 doi: 10.3390/rs10081284 – ident: ref_35 doi: 10.3390/rs11050552 – volume: 521 start-page: 436 year: 2015 ident: ref_46 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 16 start-page: 321 year: 2002 ident: ref_54 article-title: SMOTE: Synthetic minority over-sampling technique. J publication-title: Artif. Intell. Res. doi: 10.1613/jair.953 – volume: 32 start-page: 8331 year: 2011 ident: ref_18 article-title: Semi-automatic road tracking by template matching and distance transformation in urban areas publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2010.540587 – volume: 16 start-page: 699 year: 2002 ident: ref_5 article-title: Quantitative measures for spatial information of maps publication-title: Int. J. Geogr. Inf. Sci. doi: 10.1080/13658810210149416 – volume: 2016 start-page: 1 year: 2016 ident: ref_12 article-title: Multiple object extraction from aerial imagery with convolutional neural networks publication-title: J. Electron. Imaging – ident: ref_24 doi: 10.1109/ICCV.2015.178 – volume: 46 start-page: 1978 year: 2017 ident: ref_16 article-title: Road Extraction from High-spatial-resolution Remote Sensing Image by Combining with GVF Snake with Salient Features publication-title: Acta Geod. Cartogr. Sin. – volume: 5 start-page: 209 year: 2009 ident: ref_3 article-title: Automatic Road Network Recognition and Extraction for Urban Planning publication-title: Int. J. Appl. Sci. Eng. Technol. – volume: 30 start-page: 1977 year: 2009 ident: ref_8 article-title: Road centreline extraction from high—Resolution imagery based on multiscale structural features and support vector machines publication-title: Int. J. Remote Sens. doi: 10.1080/01431160802546837 – volume: 35 start-page: 1915 year: 2012 ident: ref_23 article-title: Learning hierarchical features for scene labeling publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2012.231 – ident: ref_25 doi: 10.1109/IGARSS.2016.7729408 – volume: 44 start-page: 217 year: 2016 ident: ref_21 article-title: Region-based urban road extraction from VHR satellite images using binary partition tree publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 59 start-page: 7209 year: 2019 ident: ref_36 article-title: Road Detection and Centerline Extraction Via Deep Recurrent Convolutional Neural Network U-Net publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2019.2912301 – ident: ref_49 doi: 10.1109/CVPR.2016.90 – ident: ref_42 doi: 10.1109/CVPR.2015.7298965 – ident: ref_50 doi: 10.1109/CVPR.2017.243 – ident: ref_55 doi: 10.1109/ICCV.2013.175 – ident: ref_34 doi: 10.3390/rs11080930 – ident: ref_58 doi: 10.1007/978-3-030-01234-2_49 – ident: ref_26 doi: 10.3115/v1/D14-1181 – ident: ref_29 doi: 10.3390/rs11091015 – ident: ref_59 doi: 10.1007/978-3-030-01261-8_20 – ident: ref_60 – ident: ref_22 – volume: 15 start-page: 749 year: 2018 ident: ref_37 article-title: Road extraction by deep residual U-Net publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2018.2802944 |
| SSID | ssj0000331904 |
| Score | 2.5022376 |
| Snippet | Road network extraction is one of the significant assignments for disaster emergency response, intelligent transportation systems, and real-time updating road... |
| SourceID | doaj proquest crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| StartPage | 2499 |
| SubjectTerms | Algorithms artificial intelligence Automation buildings Computer applications data collection denseunet Disaster management Emergency preparedness Emergency response Emergency vehicles High resolution high-resolution remote sensing imagery Image resolution Image segmentation Intelligent transportation systems Learning algorithms Machine learning Methods multi-scale Neural networks Parameter robustness Remote sensing road extraction roads Roads & highways Semantics Skips Transportation networks Urban areas |
| SummonAdditionalLinks | – databaseName: Proquest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Nb9QwELVge4AL4lMsLcgILhyseuPYjk-IwlYFiQUtrNRbZDvj9gBJu0kR_HtmNt6tEIhjklEO4_HMmxn7DWMvC-NNEyojZtEFUSIEEE6BFjE0hYoYIsaO7seFOVmVH071aS649flY5dYnbhx100WqkR8i9KUuIMaX1xeXgqZGUXc1j9C4yfbQBVfVhO0dzRefl7sqi1RoYrIceUkV5veH6x4RBk1cdn9Eog1h_1_-eBNkju-yOxkd8jfjct5jN6C9z27lQeXnvx6wT8vON3z-c1iPFxJ4lzgd1RBUhh-NiC8B1Q_8Cx1Nb8_4--_oMnr-Dk3tBzSc7pPgQ9vDagHDQ7Y6nn99eyLyTAQRSyMHYfUsJqWjS0WlUwgpyuCVt9E4ILYuYouRKZVWuQYzLShAWnBVBclJ0yitHrFJ27XwmPHgI2jqI3qlS6u1DzMdZLLBmmhjYabs1VY_dcyE4TS34luNiQPpsr7W5ZS92MlejDQZ_5Q6IjXvJIjaevOiW5_VeafUWgXQgKjOYvYWtfcyzQofEMmGErFlNWUH20Wq837r62vrmLLnu8-4U6j94VvorlDGVdpJ4uF98v9f7LPbCIscRahCH7DJsL6Cpwg9hvAs29dvw3PXqQ priority: 102 providerName: ProQuest |
| Title | Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet |
| URI | https://www.proquest.com/docview/2550280818 https://www.proquest.com/docview/2985909922 https://doaj.org/article/53be5e6107014c5aa0f12ab099b45758 |
| Volume | 11 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwEB1BOcCl4lMslJURXDhE9caxHR9buktBdEFbVuotsp1xOUC22k0R_HtmknQpAokLpyjxHKyZsedNxn4D8DI33tShNNkkupAVBAEyp1BnMdS5ihQi-oruydwcL4t3Z_rsWqsvPhPW0wP3itvXKqBGCvKWwHzU3ss0yX0gYBMKghrdNV-KedeSqW4PVuRasuj5SBXl9fvrDSEL7rTsfotAHVH_H_twF1xmd2F3QIXioJ_NPbiBzX24PTQo__zjAXxYrHwtpt_bdX8RQayS4CMaGf9-751HLJDUjuKUj6Q35-LtV9oqNuKIXOwb1oLvkdBLs8HlHNuHsJxNP70-zoZeCFksjGwzqycxKR1dykudQkhRBq-8jcYhs3QxS4xMqbDK1ZRhYY7SoitLTE6aWmn1CHaaVYOPQQQfUXP90CtdWK19mOggkw3WRBtzM4JXV_qp4kAUzv0qvlSUMLAuq1-6HMGLrexFT4_xV6lDVvNWgimtuw9k6GowdPUvQ49g78pI1bDONhUlRFwbJtQxgufbYVohXPbwDa4uScaV2knm333yP-bxFO4QaHIcv3K9Bzvt-hKfETBpwxhulrM3Y7h1cHTy_pSeh9P5x8W488yfZEniFw |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELaqcigXxFNsKWAEHDhE9caxEx8QAtpll7aLVLpSb8F2xuVAk3aTAv1T_EZm8tgKgbj1mGSUw_jzzDcezwxjL2JtdeEyHY29cVGCFCAyElTkXRFLjy6iy-gezPV0kXw8Vsdr7NdQC0PXKgeb2BrqovJ0Rr6N1JeygOhf3pydRzQ1irKrwwiNDhZ7cPkDQ7b69WwH1_dlHE92j95Po36qQOQTLZooVWMfpPImxJkKzgUvnJU29doA9buifisihCSVpsBYBWIQKZgsg2CELtopEWjybyQSPTlVpk8-rM50hERAi6TrgorfxfayRj5D853NH36vHQ_wl_VvXdrkNrvVc1H-tgPPHbYG5V220Y9F_3p5j306rGzBd382y678gVeB08WQiA79O8jyQ8DFBv6ZLsKXJ3x2igaq5jsI7O9QcKpewYeyhsUcmvtscS26esDWy6qEh4w760FR1tJKlaRKWTdWToTUpdqnPtYj9mrQT-779uQ0JeNbjmEK6TK_0uWIPV_JnnVNOf4p9Y7UvJKgRtrti2p5kvf7MlfSgQLkkCnGil5ZK8I4tg55s0uQyWYjtjUsUt7v7jq_wuKIPVt9xn1JyRZbQnWBMiZTRlDX383__-Ip25geHezn-7P53iN2EwmZId8Yqy223iwv4DGSnsY9aZHG2ZfrhvZvAvoSyg |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELaqrQRcEE-xtIARcOAQrdeO4_iAEGV31aWwVAsr9Zbajl0ONCmbFOhf49cxk8dWCMStxySjHMafZ77xjGcIec4Tk-Q2TaKx0zaKgQJEWngZOZtz4cBFtBndD4tkfxW_O5JHW-RXfxcGyyp7m9gY6rx0eEY-AuqLWUDwL6PQlUUcTmavz75FOEEKM639OI0WIgf-4geEb9Wr-QTW-gXns-nnt_tRN2EgcnHC6kjJsQtCOh14KoO1wTFrhFEu0R57X2HvFRZCrITOIW7x3DPldZr6oFmSNxMjwPxvK4yKBmR7b7o4XG5OeJgAeLO47YkqhGajdQXsBqc96z-8YDMs4C9f0Di42S1ys2Om9E0Lpdtkyxd3yPVuSPqXi7vk47I0OZ3-rNftZQhaBoplIhGmAFoA06WHpff0E5bFFyd0fgrmqqITgPl3n1O8ywIPReVXC1_fI6sr0dZ9MijKwj8g1BrnJeYwjZCxktLYsbQsKKsSpxxPhuRlr5_Mdc3KcWbG1wyCFtRldqnLIXm2kT1rW3T8U2oP1byRwLbazYtyfZJ1uzSTwnrpgVEqiBydNIaFMTcWWLSNgdemQ7LbL1LW7fUqu0TmkDzdfIZdiqkXU_jyHGR0KjXDHsAP__-LJ-QawDp7P18c7JAbwM40Okoud8mgXp_7R8CAavu4gxolx1eN7t9Soxhc |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Road+Extraction+of+High-Resolution+Remote+Sensing+Images+Derived+from+DenseUNet&rft.jtitle=Remote+sensing+%28Basel%2C+Switzerland%29&rft.au=Xin%2C+Jiang&rft.au=Zhang%2C+Xinchang&rft.au=Zhang%2C+Zhiqiang&rft.au=Fang%2C+Wu&rft.date=2019-10-25&rft.issn=2072-4292&rft.eissn=2072-4292&rft.volume=11&rft.issue=21&rft.spage=2499&rft_id=info:doi/10.3390%2Frs11212499&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_rs11212499 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2072-4292&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2072-4292&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2072-4292&client=summon |