{"id":"https://openalex.org/W3132505321","doi":"https://doi.org/10.1109/tgrs.2021.3059088","title":"Weakly Supervised Road Segmentation in High-Resolution Remote Sensing Images Using Point Annotations","display_name":"Weakly Supervised Road Segmentation in High-Resolution Remote Sensing Images Using Point Annotations","publication_year":2021,"publication_date":"2021-02-25","ids":{"openalex":"https://openalex.org/W3132505321","doi":"https://doi.org/10.1109/tgrs.2021.3059088","mag":"3132505321"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2021.3059088","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-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/A5067280584","display_name":"Renbao Lian","orcid":"https://orcid.org/0000-0002-0226-6377"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]},{"id":"https://openalex.org/I4210103621","display_name":"Fujian Jiangxia University","ror":"https://ror.org/01cyb5v38","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210103621"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Renbao Lian","raw_affiliation_strings":["Collage of Electronics and Information Science, Fujian Jiangxia University, Fuzhou, China","Collage of Physics and Information Engineering, Fuzhou University, Fuzhou, China","Digital Fujian, Internet-of-Things Key Laboratory of Information Collection and Processing in Smart Home, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"Collage of Physics and Information Engineering, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]},{"raw_affiliation_string":"Digital Fujian, Internet-of-Things Key Laboratory of Information Collection and Processing in Smart Home, Fuzhou, China","institution_ids":[]},{"raw_affiliation_string":"Collage of Electronics and Information Science, Fujian Jiangxia University, Fuzhou, China","institution_ids":["https://openalex.org/I4210103621"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073494285","display_name":"Liqin Huang","orcid":"https://orcid.org/0000-0001-8602-6380"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liqin Huang","raw_affiliation_strings":["Collage of Physics and Information Engineering, Fuzhou University, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"Collage of Physics and Information Engineering, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.503,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.99991,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":95},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"13"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automatic Road Extraction from Remote Sensing Images","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":"Automatic Road Extraction from Remote Sensing Images","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":"Mapping Forests with Lidar Remote Sensing","score":0.9983,"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/T11606","display_name":"Automated Pavement Inspection and Maintenance","score":0.9846,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/defect-detection","display_name":"Defect Detection","score":0.580386},{"id":"https://openalex.org/keywords/road-extraction","display_name":"Road Extraction","score":0.559262},{"id":"https://openalex.org/keywords/crack-detection","display_name":"Crack Detection","score":0.534796},{"id":"https://openalex.org/keywords/high-resolution-imagery","display_name":"High-Resolution Imagery","score":0.513863},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep Learning","score":0.510823}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.81521064},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7479336},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7272975},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6479361},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5437381},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5068129},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.49302194},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46020526},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.45950246},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.42639065},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42091617},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41986397}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2021.3059088","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.56,"display_name":"Sustainable cities and communities"}],"grants":[{"funder":"https://openalex.org/F4320321893","funder_display_name":"Department of Education, Fujian Province","award_id":"JT180595"},{"funder":"https://openalex.org/F4320327796","funder_display_name":"Science and Technology Planning Project of Fuzhou","award_id":"2020-G-066"}],"datasets":[],"versions":[],"referenced_works_count":35,"referenced_works":["https://openalex.org/W1783315696","https://openalex.org/W1903029394","https://openalex.org/W2098180043","https://openalex.org/W2108598243","https://openalex.org/W2119821739","https://openalex.org/W2124351162","https://openalex.org/W2139478903","https://openalex.org/W2155226776","https://openalex.org/W2194775991","https://openalex.org/W2203062554","https://openalex.org/W2221898772","https://openalex.org/W2304676573","https://openalex.org/W2306289963","https://openalex.org/W2307770531","https://openalex.org/W2337429362","https://openalex.org/W2342699585","https://openalex.org/W2465439865","https://openalex.org/W2547880720","https://openalex.org/W2552414813","https://openalex.org/W2593886839","https://openalex.org/W2623331213","https://openalex.org/W2638069280","https://openalex.org/W2742398205","https://openalex.org/W2774320778","https://openalex.org/W2780861787","https://openalex.org/W2886773299","https://openalex.org/W2943295486","https://openalex.org/W2945957599","https://openalex.org/W2967073193","https://openalex.org/W2981329832","https://openalex.org/W3018914855","https://openalex.org/W3024167159","https://openalex.org/W4239944110","https://openalex.org/W611457968","https://openalex.org/W73112891"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4285411112","https://openalex.org/W2922442631","https://openalex.org/W2171299904","https://openalex.org/W2104657898","https://openalex.org/W2090763504","https://openalex.org/W2085033728","https://openalex.org/W2053596378","https://openalex.org/W1948992892","https://openalex.org/W148178222"],"abstract_inverted_index":{"Road":[0],"segmentation":[1,52],"methods":[2,190],"based":[3,155],"on":[4,156],"deep":[5,65],"neural":[6,67],"networks":[7],"have":[8],"achieved":[9],"great":[10],"success":[11],"in":[12,57,86],"recent":[13],"years,":[14],"but":[15],"creating":[16],"accurate":[17],"pixel-wise":[18],"training":[19],"labels":[20],"is":[21,142,162],"still":[22],"a":[23,48,63,96,108,135,200],"boring":[24],"and":[25,74,78,100,129,137,175,196],"expensive":[26],"task,":[27],"especially":[28],"for":[29,42,71,114],"large-scale":[30],"high-resolution":[31],"remote":[32],"sensing":[33],"images":[34],"(HRSIs).":[35],"Inspired":[36],"by":[37],"the":[38,80,87,92,124,130,147,151,166,170,186,193],"stacked":[39],"hourglass":[40],"model":[41,70,81,94,154],"human":[43],"joints":[44],"detection,":[45],"we":[46,61],"propose":[47],"weakly":[49],"supervised":[50,188],"road":[51,72,90,99,119,128,148,167,172],"method":[53,181],"using":[54,82],"point":[55,83],"annotations":[56],"this":[58],"article.":[59],"First,":[60],"design":[62],"patch-based":[64],"convolutional":[66],"network":[68],"(DCNN)":[69],"seeds":[73],"background":[75,101],"points":[76,102],"detection":[77],"train":[79,107],"annotations.":[84],"Then,":[85],"process":[88],"of":[89,98,127,133],"segmentation,":[91],"DCNN":[93],"detects":[95],"series":[97],"that":[103,179],"are":[104],"used":[105],"to":[106,123,145,164,185],"Support":[109],"Vector":[110],"Machine":[111],"Classifier":[112],"(SVC)":[113],"classifying":[115],"each":[116],"pixel":[117],"into":[118],"or":[120],"nonroad.":[121],"According":[122],"local":[125,157],"geometry":[126],"inaccurate":[131],"classification":[132],"SVC,":[134],"multiscale":[136],"multidirection":[138],"Gabor":[139],"filter":[140],"(MMGF)":[141],"put":[143],"forward":[144],"estimate":[146],"potential.":[149,173],"Finally,":[150],"active":[152],"contour":[153],"binary":[158],"fitting":[159],"energy":[160],"(LBF-Snake)":[161],"introduced":[163],"extract":[165],"regions":[168],"from":[169],"inhomogeneous":[171],"Qualitative":[174],"quantitative":[176],"comparisons":[177],"show":[178],"our":[180],"achieves":[182],"results":[183],"close":[184],"fully":[187],"semantic":[189],"without":[191],"considering":[192],"annotation":[194],"cost":[195],"outperforms":[197],"them":[198],"given":[199],"fixed":[201],"budget.":[202]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3132505321","counts_by_year":[{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":1}],"updated_date":"2024-12-03T09:18:08.581116","created_date":"2021-03-01"}
