{"id":"https://openalex.org/W4415594373","doi":"https://doi.org/10.1109/tgrs.2025.3625550","title":"CFIFNet: A Road Extraction Network Through Cross-Modal Frequency-Domain Interaction and Fusion From High-Resolution Remote Sensing Image and GPS Trajectory/LiDAR","display_name":"CFIFNet: A Road Extraction Network Through Cross-Modal Frequency-Domain Interaction and Fusion From High-Resolution Remote Sensing Image and GPS Trajectory/LiDAR","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415594373","doi":"https://doi.org/10.1109/tgrs.2025.3625550"},"language":null,"primary_location":{"id":"doi:10.1109/tgrs.2025.3625550","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3625550","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","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/A5063514190","display_name":"Yiqing Zhou","orcid":"https://orcid.org/0009-0007-1988-2567"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiqing Zhou","raw_affiliation_strings":["School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China","School of Electronic and Information, Northwestern Polytechnical University, Xi’an, China"],"raw_orcid":"https://orcid.org/0009-0007-1988-2567","affiliations":[{"raw_affiliation_string":"School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"School of Electronic and Information, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101041741","display_name":"Wenjing Cai","orcid":"https://orcid.org/0000-0002-9175-8604"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenjing Cai","raw_affiliation_strings":["School of Cybersecurity, Northwestern Polytechnical University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0002-9175-8604","affiliations":[{"raw_affiliation_string":"School of Cybersecurity, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020820957","display_name":"Lipeng Gao","orcid":"https://orcid.org/0000-0003-0026-3719"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lipeng Gao","raw_affiliation_strings":["School of Software, Northwestern Polytechnical University, Xi’an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113417592","display_name":"Xingke Hao","orcid":null},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingke Hao","raw_affiliation_strings":["School of Software, Northwestern Polytechnical University, Xi’an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.33091088,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"63","issue":null,"first_page":"1","last_page":"17"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9980999827384949,"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":0.9980999827384949,"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":0.9915000200271606,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.949400007724762,"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/wavelet-transform","display_name":"Wavelet transform","score":0.5407000184059143},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.5379999876022339},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.532800018787384},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5101000070571899},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.5011000037193298},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4431999921798706},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4339999854564667},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.40299999713897705},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4007999897003174},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.3862000107765198}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8052999973297119},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5568000078201294},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5407000184059143},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.5379999876022339},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.532800018787384},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5220999717712402},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5138999819755554},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5101000070571899},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.5011000037193298},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4431999921798706},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4339999854564667},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.40299999713897705},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4007999897003174},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.3862000107765198},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.37450000643730164},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.35040000081062317},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.34709998965263367},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3292999863624573},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.3001999855041504},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C12957241","wikidata":"https://www.wikidata.org/wiki/Q15903792","display_name":"GPS signals","level":4,"score":0.2822999954223633},{"id":"https://openalex.org/C75172450","wikidata":"https://www.wikidata.org/wiki/Q623950","display_name":"Fast Fourier transform","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.2770000100135803},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2680000066757202},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.25920000672340393},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2025.3625550","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3625550","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2724881070","display_name":null,"funder_award_id":"Grant SKLGIE2021-Z-4-1","funder_id":"https://openalex.org/F4320326985","funder_display_name":"State Key Laboratory of Geo-Information Engineering"},{"id":"https://openalex.org/G2807884033","display_name":null,"funder_award_id":"2023A1515111175","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G3848296118","display_name":null,"funder_award_id":"D5000210596","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G753540652","display_name":null,"funder_award_id":"42101469","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8068212329","display_name":null,"funder_award_id":"2020A1515111158","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320326985","display_name":"State Key Laboratory of Geo-Information Engineering","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2156746722","https://openalex.org/W2395611524","https://openalex.org/W2893801697","https://openalex.org/W2901550124","https://openalex.org/W2943898693","https://openalex.org/W2946824094","https://openalex.org/W2962858109","https://openalex.org/W2998048830","https://openalex.org/W2999281281","https://openalex.org/W3035012694","https://openalex.org/W3108601100","https://openalex.org/W3177052299","https://openalex.org/W3190518009","https://openalex.org/W3203828160","https://openalex.org/W3214248441","https://openalex.org/W4200512530","https://openalex.org/W4214666412","https://openalex.org/W4225648150","https://openalex.org/W4225725240","https://openalex.org/W4283450732","https://openalex.org/W4285114932","https://openalex.org/W4292656663","https://openalex.org/W4312960790","https://openalex.org/W4313436932","https://openalex.org/W4317603812","https://openalex.org/W4320515785","https://openalex.org/W4320919181","https://openalex.org/W4327620107","https://openalex.org/W4385413647","https://openalex.org/W4385463122","https://openalex.org/W4385748513","https://openalex.org/W4385938250","https://openalex.org/W4386179772","https://openalex.org/W4387829370","https://openalex.org/W4387986940","https://openalex.org/W4391097076","https://openalex.org/W4391566611","https://openalex.org/W4391791466","https://openalex.org/W4391992568","https://openalex.org/W4392543906","https://openalex.org/W4393372343","https://openalex.org/W4393372344","https://openalex.org/W4394002411","https://openalex.org/W4396753458","https://openalex.org/W4396909879","https://openalex.org/W4398788587","https://openalex.org/W4399527391","https://openalex.org/W4400579361","https://openalex.org/W4402775847","https://openalex.org/W4405361114","https://openalex.org/W4405632860","https://openalex.org/W4412171101"],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,19,206,218],"deep":[3],"learning":[4],"have":[5],"significantly":[6],"improved":[7],"road":[8,171,177,193,220],"extraction":[9,88],"from":[10],"high-resolution":[11,69],"remote":[12],"sensing":[13],"(RS)":[14],"images,":[15],"yet":[16],"challenges":[17],"persist":[18],"scenarios":[20],"with":[21,147],"severe":[22],"obstructions":[23],"(e.g.,":[24],"forest":[25],"canopies":[26],"or":[27],"dense":[28],"fog)":[29],"where":[30],"semantic":[31,149],"information":[32],"is":[33,167],"highly":[34,90],"limited.":[35],"To":[36,85],"address":[37],"these":[38],"issues":[39],"while":[40],"overcoming":[41],"the":[42,82,95,98,131,161],"parameter":[43],"inefficiency":[44],"and":[45,59,66,72,76,122,157,182,187,198,214],"suboptimal":[46],"performance":[47],"of":[48,89,154,201],"existing":[49],"multi-modal":[50,110,192],"approaches,":[51],"we":[52],"propose":[53],"a":[54,199],"lightweight":[55],"Cross-modal":[56],"Frequency-domain":[57,133],"Interaction":[58,100],"Fusion":[60,134],"network":[61],"(CFIFNet)":[62],"for":[63],"effective":[64],"interaction":[65,119],"fusion":[67,139],"between":[68],"RS":[70],"images":[71,79],"GPS":[73],"trajectory/Light":[74],"Detection":[75],"Ranging":[77],"(LiDAR)":[78],"based":[80],"on":[81,190],"frequency":[83],"domain.":[84],"facilitate":[86],"accurate":[87],"similar":[91],"road-background":[92],"edge":[93,172],"regions,":[94],"model":[96],"incorporates":[97],"Wavelet":[99],"Module":[101],"(WIM),":[102],"which":[103],"uses":[104],"discrete":[105],"wavelet":[106],"transform":[107],"to":[108,151,169],"decompose":[109],"inputs":[111],"into":[112],"high/low-frequency":[113],"components\u2014with":[114],"high-frequency":[115],"features":[116,178],"undergoing":[117,125],"global":[118],"via":[120,179],"Transformer":[121],"low-frequency":[123],"components":[124],"local":[126],"channel-spatial":[127],"dual-branch":[128],"interaction.":[129],"Additionally,":[130],"Fourier":[132],"(FFF)":[135],"module":[136],"enables":[137],"efficient":[138],"through":[140],"joint":[141],"amplitude-phase":[142],"manipulation,":[143],"generating":[144],"feature":[145],"maps":[146],"enhanced":[148],"discrimination":[150],"improve":[152],"detection":[153],"small":[155],"roads":[156],"road-like":[158],"objects.":[159],"Moreover,":[160],"Adaptive":[162],"Frequency":[163],"Convolution":[164],"Attention":[165],"(AFCA)":[166],"integrated":[168],"alleviate":[170],"occlusion":[173],"by":[174],"serially":[175],"enhancing":[176],"dual-domain":[180],"(channel":[181],"frequency)":[183],"refinement.":[184],"Evaluation":[185],"metrics":[186],"visualization":[188],"results":[189],"multiple":[191],"datasets":[194],"validate":[195],"CFIFNet\u2019s":[196],"effectiveness,":[197],"series":[200],"experiments":[202],"demonstrate":[203],"its":[204],"efficiency":[205],"aligning":[207],"cross-modal":[208],"features,":[209],"avoiding":[210],"inter-modal":[211],"noise":[212],"interference,":[213],"achieving":[215],"complementary":[216],"advantages":[217],"multi-source":[219],"scenes.":[221]},"counts_by_year":[],"updated_date":"2025-11-06T23:17:08.748858","created_date":"2025-10-28T00:00:00"}