{"id":"https://openalex.org/W2900518108","doi":"https://doi.org/10.1109/lgrs.2018.2878771","title":"Road Segmentation Based on Hybrid Convolutional Network for High-Resolution Visible Remote Sensing Image","display_name":"Road Segmentation Based on Hybrid Convolutional Network for High-Resolution Visible Remote Sensing Image","publication_year":2018,"publication_date":"2018-11-21","ids":{"openalex":"https://openalex.org/W2900518108","doi":"https://doi.org/10.1109/lgrs.2018.2878771","mag":"2900518108"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2018.2878771","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2018.2878771","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100339270","display_name":"Ye Li","orcid":"https://orcid.org/0000-0003-1303-7219"},"institutions":[{"id":"https://openalex.org/I4210086028","display_name":"Technology and Engineering Center for Space Utilization","ror":"https://ror.org/00cn03n83","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210086028"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ye Li","raw_affiliation_strings":["Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210086028","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064138796","display_name":"Lili Guo","orcid":"https://orcid.org/0000-0002-0470-692X"},"institutions":[{"id":"https://openalex.org/I4210086028","display_name":"Technology and Engineering Center for Space Utilization","ror":"https://ror.org/00cn03n83","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210086028"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lili Guo","raw_affiliation_strings":["Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210086028","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111010103","display_name":"Jun Rao","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210086028","display_name":"Technology and Engineering Center for Space Utilization","ror":"https://ror.org/00cn03n83","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210086028"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Rao","raw_affiliation_strings":["Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210086028","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101733448","display_name":"Lele Xu","orcid":"https://orcid.org/0000-0002-5000-5788"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210086028","display_name":"Technology and Engineering Center for Space Utilization","ror":"https://ror.org/00cn03n83","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210086028"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lele Xu","raw_affiliation_strings":["Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210086028","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011099007","display_name":"Shan Jin","orcid":"https://orcid.org/0000-0001-9591-8866"},"institutions":[{"id":"https://openalex.org/I4210086028","display_name":"Technology and Engineering Center for Space Utilization","ror":"https://ror.org/00cn03n83","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210086028"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shan Jin","raw_affiliation_strings":["Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210086028","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100339270"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210086028"],"apc_list":null,"apc_paid":null,"fwci":5.0949,"has_fulltext":false,"cited_by_count":50,"citation_normalized_percentile":{"value":0.95432495,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"16","issue":"4","first_page":"613","last_page":"617"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9886000156402588,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7971978187561035},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7756404876708984},{"id":"https://openalex.org/keywords/subnetwork","display_name":"Subnetwork","score":0.747795820236206},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.693355143070221},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5990064740180969},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5929350256919861},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4960080087184906},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.49000564217567444},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.47176268696784973},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4620397090911865},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4505959451198578},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4304599165916443},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.42934805154800415},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.08217769861221313},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08051782846450806},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.06969127058982849},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.05518659949302673}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7971978187561035},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7756404876708984},{"id":"https://openalex.org/C2780186347","wikidata":"https://www.wikidata.org/wiki/Q11414","display_name":"Subnetwork","level":2,"score":0.747795820236206},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.693355143070221},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5990064740180969},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5929350256919861},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4960080087184906},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.49000564217567444},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.47176268696784973},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4620397090911865},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4505959451198578},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4304599165916443},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.42934805154800415},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.08217769861221313},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08051782846450806},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.06969127058982849},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.05518659949302673},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2018.2878771","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2018.2878771","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8299999833106995,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G322634008","display_name":null,"funder_award_id":"61501434","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1535289548","https://openalex.org/W1686810756","https://openalex.org/W1901129140","https://openalex.org/W2097375363","https://openalex.org/W2253590344","https://openalex.org/W2304676573","https://openalex.org/W2314029052","https://openalex.org/W2395611524","https://openalex.org/W2464204616","https://openalex.org/W2547880720","https://openalex.org/W2577537809","https://openalex.org/W2588561483","https://openalex.org/W2593886839","https://openalex.org/W2594652239","https://openalex.org/W2613625996","https://openalex.org/W2615237590","https://openalex.org/W2616755213","https://openalex.org/W2623331213","https://openalex.org/W2737129951","https://openalex.org/W2774320778","https://openalex.org/W2950094539","https://openalex.org/W2962835968","https://openalex.org/W2963004077","https://openalex.org/W2963881378","https://openalex.org/W3100521496","https://openalex.org/W4248710273","https://openalex.org/W6637373629","https://openalex.org/W6639824700","https://openalex.org/W7020680850"],"related_works":["https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W3144569342","https://openalex.org/W4313052709","https://openalex.org/W2945274617","https://openalex.org/W2022929107","https://openalex.org/W2055202857","https://openalex.org/W1999008862","https://openalex.org/W4205800335","https://openalex.org/W2758994127"],"abstract_inverted_index":{"Road":[0],"segmentation":[1,19,39,103,117,175,181],"plays":[2],"an":[3],"important":[4],"role":[5],"in":[6,58,75,132],"many":[7],"applications,":[8],"such":[9],"as":[10],"intelligent":[11],"transportation":[12],"system":[13],"and":[14,55,88,99,137,154],"urban":[15],"planning.":[16],"Various":[17],"road":[18,38,102,121,174],"methods":[20],"have":[21],"been":[22],"proposed":[23,74,163],"for":[24,119],"visible":[25,42],"remote":[26,43],"sensing":[27,44],"images,":[28],"especially":[29],"the":[30,106,162],"popular":[31],"convolutional":[32,67,83,111],"neural":[33],"network-based":[34],"methods.":[35],"However,":[36],"high-accuracy":[37],"from":[40,124],"high-resolution":[41],"images":[45],"is":[46,73],"still":[47],"a":[48,65,81,85,89,95,97,100,109],"challenging":[49],"problem":[50],"due":[51],"to":[52,113,160],"complex":[53,138],"background":[54],"multiscale":[56,135],"roads":[57,136],"these":[59,92,115],"images.":[60],"To":[61],"handle":[62],"this":[63,76],"problem,":[64],"hybrid":[66],"network":[68],"(HCN),":[69],"fusing":[70],"multiple":[71],"subnetworks,":[72],"letter.":[77],"The":[78],"HCN":[79,107,128,164,178],"contains":[80],"fully":[82],"network,":[84],"modified":[86],"U-Net,":[87],"VGG":[90],"subnetwork;":[91],"subnetworks":[93],"obtain":[94],"coarse-grained,":[96],"medium-grained,":[98],"fine-grained":[101],"map.":[104],"Moreover,":[105],"uses":[108],"shallow":[110],"subnetwork":[112],"fuse":[114],"multigrained":[116,125],"maps":[118],"final":[120],"segmentation.":[122],"Benefitting":[123],"segmentation,":[126],"our":[127,177],"shows":[129],"impressing":[130],"results":[131],"processing":[133],"both":[134],"background.":[139],"Four":[140],"testing":[141,167],"indicators,":[142],"including":[143],"pixel":[144],"accuracy,":[145,147],"mean":[146,148],"region":[149],"intersection":[150],"over":[151],"union":[152],"(IU),":[153],"frequency":[155],"weighted":[156],"IU,":[157],"are":[158],"computed":[159],"evaluate":[161],"on":[165],"two":[166],"data":[168],"sets.":[169],"Compared":[170],"with":[171],"five":[172],"state-of-the-art":[173],"methods,":[176],"has":[179],"higher":[180],"accuracy":[182],"than":[183],"them.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}