{"id":"https://openalex.org/W4387802769","doi":"https://doi.org/10.1109/igarss52108.2023.10283444","title":"Multi-Scale Fusion Attention Network for Multispectral Worldview3 Data Road Segmentation","display_name":"Multi-Scale Fusion Attention Network for Multispectral Worldview3 Data Road Segmentation","publication_year":2023,"publication_date":"2023-07-16","ids":{"openalex":"https://openalex.org/W4387802769","doi":"https://doi.org/10.1109/igarss52108.2023.10283444"},"language":"en","primary_location":{"id":"doi:10.1109/igarss52108.2023.10283444","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10283444","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-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/A5001707497","display_name":"Zhonggui Tong","orcid":"https://orcid.org/0000-0001-9380-4806"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhonggui Tong","raw_affiliation_strings":["University of Electronic Science and Technology of China,School of Automation Engineering,Chengdu,China,611731"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,School of Automation Engineering,Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100736194","display_name":"Yuxia Li","orcid":"https://orcid.org/0000-0001-7154-3641"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxia Li","raw_affiliation_strings":["University of Electronic Science and Technology of China,School of Automation Engineering,Chengdu,China,611731"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,School of Automation Engineering,Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100687474","display_name":"Jinglin Zhang","orcid":"https://orcid.org/0000-0001-7499-1992"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinglin Zhang","raw_affiliation_strings":["University of Electronic Science and Technology of China,School of Automation Engineering,Chengdu,China,611731"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,School of Automation Engineering,Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042857054","display_name":"Yushu Gong","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yushu Gong","raw_affiliation_strings":["University of Electronic Science and Technology of China,School of Automation Engineering,Chengdu,China,611731"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,School of Automation Engineering,Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007019142","display_name":"Lei He","orcid":"https://orcid.org/0000-0002-9875-9853"},"institutions":[{"id":"https://openalex.org/I24201400","display_name":"Chengdu University of Information Technology","ror":"https://ror.org/01yxwrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I24201400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei He","raw_affiliation_strings":["Chengdu University of Information Technology,School of Software Engineering,Chengdu,China,610025"],"affiliations":[{"raw_affiliation_string":"Chengdu University of Information Technology,School of Software Engineering,Chengdu,China,610025","institution_ids":["https://openalex.org/I24201400"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5001707497"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19506076,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5720","last_page":"5723"},"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":0.9879999756813049,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9585000276565552,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/computer-science","display_name":"Computer science","score":0.7693683505058289},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.7616943120956421},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.7397769093513489},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7033980488777161},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.666491687297821},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6202420592308044},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5558261275291443},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5375300049781799},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5289768576622009},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5189647078514099},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.47899433970451355},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4786832928657532},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.4308944046497345},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4164920747280121},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3811667263507843},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09582290053367615},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07288610935211182},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07250848412513733},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.06799545884132385}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7693683505058289},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.7616943120956421},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.7397769093513489},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7033980488777161},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.666491687297821},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6202420592308044},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5558261275291443},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5375300049781799},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5289768576622009},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5189647078514099},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.47899433970451355},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4786832928657532},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.4308944046497345},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4164920747280121},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3811667263507843},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09582290053367615},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07288610935211182},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07250848412513733},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.06799545884132385},{"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss52108.2023.10283444","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10283444","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.75,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322108","display_name":"Ministry of Science and Technology","ror":"https://ror.org/032e49973"},{"id":"https://openalex.org/F4320329861","display_name":"Natural Science Foundation of Sichuan Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2811199523","https://openalex.org/W2893801697","https://openalex.org/W2940262938","https://openalex.org/W2955058313","https://openalex.org/W3121619677","https://openalex.org/W3159533239","https://openalex.org/W3207307521","https://openalex.org/W4296106334","https://openalex.org/W6767258250","https://openalex.org/W6794808484"],"related_works":["https://openalex.org/W3000097931","https://openalex.org/W4318664220","https://openalex.org/W2354322770","https://openalex.org/W3036931590","https://openalex.org/W3089138622","https://openalex.org/W3205445068","https://openalex.org/W2792279927","https://openalex.org/W4385497869","https://openalex.org/W283587633","https://openalex.org/W3134004915"],"abstract_inverted_index":{"In":[0,121],"recent":[1],"years,":[2],"many":[3],"semantic":[4],"segmentation":[5],"methods":[6],"based":[7],"on":[8,175,184],"convolutional":[9],"neural":[10],"networks":[11],"(CNN)":[12],"have":[13],"been":[14],"applied":[15,142],"to":[16,25,51,54,75,86,103,112,129,143],"road":[17,31,38,62,132],"extraction,":[18],"but":[19],"objects":[20],"with":[21,90],"similar":[22],"spectral":[23,107],"characteristics":[24],"roads":[26],"in":[27,64,155],"RGB":[28,52,77],"images":[29,149],"and":[30,78,94,105,134,147,172,181],"occlusions":[32],"cause":[33],"the":[34,56,95,144,162,176,185],"discontinuous":[35],"output":[36],"of":[37,58,115,169],"extraction.":[39],"To":[40],"ensure":[41],"extraction":[42,63],"performance,":[43],"hyperspectral":[44],"data":[45,53],"is":[46,84,101,127],"used":[47,85,102],"as":[48],"a":[49,70,123,152,166],"supplement":[50],"improve":[55],"ability":[57],"remote":[59],"sensing":[60],"image":[61],"this":[65],"paper.":[66],"This":[67],"paper":[68],"uses":[69],"multi-scale":[71,124],"fusion":[72,114],"attention":[73,125],"network":[74],"combine":[76],"multispectral":[79,88],"imagery.":[80],"First,":[81],"band":[82],"selection":[83],"select":[87],"bands":[89],"high":[91],"inter-class":[92],"separability,":[93],"Cross-Source":[96],"Feature":[97],"Recalibration":[98],"Module":[99],"(CSFR)":[100],"calibrate":[104],"fuse":[106,130],"features":[108,117,133],"at":[109,118],"different":[110,119],"scales":[111],"achieve":[113],"multi-source":[116],"scales.":[120],"addition,":[122],"decoder":[126],"proposed":[128,139],"multi-level":[131],"global":[135],"context":[136],"information.":[137],"The":[138],"method":[140,158],"was":[141],"SpaceNet":[145,177],"dataset":[146,187],"self-annotated":[148,186],"from":[150],"Chongzhou,":[151],"representative":[153],"city":[154],"China.":[156],"Our":[157],"performs":[159],"better":[160],"over":[161],"baseline":[163],"HRNet":[164],"by":[165],"large":[167],"margin":[168],"+6.38":[170],"IoU":[171,180],"+5.11":[173],"F1-score":[174,183],"dataset,":[178],"+3.61":[179],"+2.32":[182],"(ChongZhou":[188],"dataset).":[189]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}