{"id":"https://openalex.org/W7155193079","doi":"https://doi.org/10.1109/tgrs.2026.3686039","title":"A Dynamic Frequency Selection Module for Geometry-Aware Building Extraction in VHR Remote Sensing Image","display_name":"A Dynamic Frequency Selection Module for Geometry-Aware Building Extraction in VHR Remote Sensing Image","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7155193079","doi":"https://doi.org/10.1109/tgrs.2026.3686039"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2026.3686039","doi":"https://doi.org/10.1109/tgrs.2026.3686039","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2026.3686039","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,"listed_in":["anvur-scientific","ccf-b","cwts-core","dongbi-b","jufo-3","norway-2","poland-200","russia-white-list-1","vabb-shw"],"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"},"volume":null,"type":"article","is_traditional":true,"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":null,"display_name":"Yi Huang","orcid":"https://orcid.org/0000-0001-7134-8328","observed_orcids":["https://orcid.org/0000-0001-7134-8328"]},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Huang","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications"],"raw_orcid":"https://orcid.org/0000-0001-7134-8328","affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037514382","display_name":"Siyu Hong","orcid":"https://orcid.org/0009-0007-2347-9631","observed_orcids":["https://orcid.org/0009-0007-2347-9631","https://orcid.org/0009-0004-8054-8136"]},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siyu Hong","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057345714","display_name":"Shaohua Lei","orcid":"https://orcid.org/0000-0002-4138-0850","observed_orcids":["https://orcid.org/0000-0002-4138-0850"]},"institutions":[{"id":"https://openalex.org/I4210111986","display_name":"Nanjing Hydraulic Research Institute","ror":"https://ror.org/02403qw73","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210111986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaohua Lei","raw_affiliation_strings":["Nanjing Hydraulic Research Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing Hydraulic Research Institute","institution_ids":["https://openalex.org/I4210111986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134232257","display_name":"Hui Chen","orcid":"https://orcid.org/0000-0002-7689-400X","observed_orcids":["https://orcid.org/0000-0002-7689-400X"]},"institutions":[{"id":"https://openalex.org/I152031979","display_name":"Nanjing Normal University","ror":"https://ror.org/036trcv74","country_code":"CN","type":"education","lineage":["https://openalex.org/I152031979"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Chen","raw_affiliation_strings":["Nanjing Normal University"],"raw_orcid":"https://orcid.org/0000-0002-7689-400X","affiliations":[{"raw_affiliation_string":"Nanjing Normal University","institution_ids":["https://openalex.org/I152031979"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100690406","display_name":"Peijun Du","orcid":"https://orcid.org/0000-0002-2488-2656","observed_orcids":["https://orcid.org/0000-0002-2488-2656"]},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peijun Du","raw_affiliation_strings":["Nanjing University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052697795","display_name":"Jinlong Sun","orcid":"https://orcid.org/0000-0002-3373-2203","observed_orcids":["https://orcid.org/0000-0002-3373-2203"]},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinlong Sun","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications"],"raw_orcid":"https://orcid.org/0000-0002-3373-2203","affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100390094","display_name":"Haitao Zhao","orcid":"https://orcid.org/0000-0002-3539-3532","observed_orcids":["https://orcid.org/0000-0002-3539-3532"]},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haitao Zhao","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications"],"raw_orcid":"https://orcid.org/0000-0002-3539-3532","affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I41198531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"64","issue":null,"first_page":"1","last_page":"11"},"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/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"}},"topics":[{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":1.0,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":9.999999747378752e-05,"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"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":9.999999747378752e-05,"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/building-footprint-extraction","display_name":"building footprint extraction","score":0.8140000104904175},{"id":"https://openalex.org/keywords/very-high-resolution-imagery","display_name":"very high resolution imagery","score":0.7160000205039978},{"id":"https://openalex.org/keywords/frequency-domain-filtering","display_name":"frequency-domain filtering","score":0.7120000123977661},{"id":"https://openalex.org/keywords/dynamic-frequency-selection","display_name":"dynamic frequency selection","score":0.7210000157356262},{"id":"https://openalex.org/keywords/boundary-localization","display_name":"boundary localization","score":0.5839999914169312},{"id":"https://openalex.org/keywords/edge-detection","display_name":"edge detection","score":0.3659999966621399},{"id":"https://openalex.org/keywords/attention-based-fusion","display_name":"attention fusion","score":0.7580000162124634},{"id":"https://openalex.org/keywords/semantic-segmentation","display_name":"semantic segmentation","score":0.7519999742507935},{"id":"https://openalex.org/keywords/urban-remote-sensing","display_name":"urban remote sensing","score":0.5019999742507935},{"id":"https://openalex.org/keywords/hausdorff-distance","display_name":"Hausdorff distance","score":0.9670000076293945}],"concepts":[{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.8220999836921692},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6496999859809875},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.5342000126838684},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46209999918937683},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4361000061035156},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4318000078201294},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.4113999903202057},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.40389999747276306},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4020000100135803},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.34549999237060547},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.34310001134872437},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.32109999656677246},{"id":"https://openalex.org/C87456703","wikidata":"https://www.wikidata.org/wiki/Q247760","display_name":"Radiometry","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.30550000071525574},{"id":"https://openalex.org/C39399123","wikidata":"https://www.wikidata.org/wiki/Q1348989","display_name":"Earth observation","level":3,"score":0.28529998660087585},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2782999873161316},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.26089999079704285}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2026.3686039","doi":"https://doi.org/10.1109/tgrs.2026.3686039","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2026.3686039","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,"listed_in":["anvur-scientific","ccf-b","cwts-core","dongbi-b","jufo-3","norway-2","poland-200","russia-white-list-1","vabb-shw"],"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":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.7646999955177307}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1745334888","https://openalex.org/W1901129140","https://openalex.org/W1910657905","https://openalex.org/W2395611524","https://openalex.org/W2412782625","https://openalex.org/W2560023338","https://openalex.org/W2626778328","https://openalex.org/W2762439315","https://openalex.org/W2938425859","https://openalex.org/W2964309882","https://openalex.org/W2996290406","https://openalex.org/W3014641072","https://openalex.org/W3097243506","https://openalex.org/W3122731052","https://openalex.org/W3127751679","https://openalex.org/W3138516171","https://openalex.org/W3161838454","https://openalex.org/W3179489280","https://openalex.org/W3202579583","https://openalex.org/W3211329537","https://openalex.org/W4210736635","https://openalex.org/W4214760051","https://openalex.org/W4226289601","https://openalex.org/W4229441694","https://openalex.org/W4281693365","https://openalex.org/W4293731355","https://openalex.org/W4312312588","https://openalex.org/W4312959398","https://openalex.org/W4313166909","https://openalex.org/W4386075642","https://openalex.org/W4388262289","https://openalex.org/W4390874575","https://openalex.org/W4391454392","https://openalex.org/W4399154429","https://openalex.org/W4399618753","https://openalex.org/W4400579361","https://openalex.org/W4402667899","https://openalex.org/W4402981192","https://openalex.org/W4403022816","https://openalex.org/W4404708688","https://openalex.org/W4405011115","https://openalex.org/W4406983111","https://openalex.org/W4407128621"],"related_works":["https://openalex.org/W4412534185","https://openalex.org/W7221970475","https://openalex.org/W4413359196","https://openalex.org/W4408048065","https://openalex.org/W4385400149","https://openalex.org/W4388574916","https://openalex.org/W4404694133","https://openalex.org/W7218845691","https://openalex.org/W4413277751","https://openalex.org/W7138882135"],"abstract_inverted_index":{"Reliable":[0],"measurement":[1],"of":[2],"building":[3,67,93,231],"footprints,":[4],"encompassing":[5],"precise":[6],"boundary":[7,157],"localization":[8],"and":[9,38,69,125,156,165,175,191],"planar":[10],"area":[11,154],"estimation,":[12],"is":[13],"fundamental":[14],"to":[15,42,51,63,117,128],"urban":[16],"applications":[17],"using":[18],"very":[19],"high-resolution":[20],"(VHR)":[21],"remote":[22,234],"sensing":[23],"imagery.":[24],"Conventional":[25],"semantic":[26],"segmentation":[27],"pipelines":[28],"optimize":[29],"pixel-wise":[30],"accuracy":[31],"but":[32],"often":[33],"suffer":[34],"from":[35],"blurred":[36],"edges":[37],"shape":[39],"distortions,":[40],"leading":[41],"biased":[43],"geometric":[44,221],"measurements.":[45],"Although":[46],"recent":[47],"frequency-domain":[48],"methods":[49],"attempt":[50],"recover":[52],"structural":[53],"details,":[54],"they":[55],"typically":[56],"rely":[57],"on":[58,161],"static":[59],"filters":[60],"that":[61,169],"fail":[62],"adapt":[64],"across":[65],"diverse":[66],"topologies":[68],"complex":[70],"background":[71],"clutter.":[72],"To":[73],"overcome":[74],"this":[75],"limitation,":[76],"we":[77],"propose":[78],"the":[79,97,113,149,162,194,216],"Dynamic":[80],"Frequency":[81],"Selection":[82],"Module":[83],"(DFSM),":[84],"a":[85,107,225],"plug-and-play":[86],"adaptive":[87],"frequency":[88,98,143],"selection":[89],"module":[90],"for":[91,121,133,220,229],"geometry-aware":[92,230],"extraction.":[94],"DFSM":[95,170,183,199,223],"parameterizes":[96],"cutoff":[99],"into":[100],"dynamically":[101],"learnable,":[102],"continuous":[103],"variables,":[104],"functioning":[105],"as":[106],"content-aware":[108],"filter.":[109],"It":[110],"adaptively":[111],"modulates":[112],"spectral":[114],"passband\u2014widening":[115],"it":[116,127],"suppress":[118],"high-frequency":[119],"noise":[120],"small,":[122],"cluttered":[123],"buildings,":[124],"narrowing":[126],"effectively":[129],"sharpen":[130],"edge":[131,207],"contours":[132],"large-scale":[134],"structures.":[135],"Subsequently,":[136],"an":[137],"attention-based":[138],"fusion":[139],"mechanism":[140],"integrates":[141],"these":[142],"representations":[144],"with":[145],"spatial":[146],"context,":[147],"optimizing":[148],"inherent":[150],"trade-off":[151],"between":[152],"regional":[153,181],"integrity":[155],"precision.":[158],"Comprehensive":[159],"evaluations":[160],"ISPRS":[163],"Vaihingen":[164],"WHU":[166],"datasets":[167],"demonstrate":[168],"consistently":[171],"enhances":[172],"both":[173],"CNN-":[174],"Transformer-based":[176],"segmenters.":[177],"Beyond":[178],"improving":[179],"conventional":[180],"metrics,":[182],"elevates":[184],"Boundary-Intersection":[185],"over":[186],"Union":[187],"(Boundary-IoU)":[188],"by":[189],"3%\u20134%":[190],"significantly":[192],"reduces":[193],"95%":[195,217],"Hausdorff":[196],"distance.":[197],"Notably,":[198],"outperforms":[200],"explicit":[201],"boundary-supervised":[202],"baselines":[203],"without":[204],"requiring":[205],"auxiliary":[206],"annotations":[208],"or":[209],"multi-task":[210],"loss":[211],"tuning.":[212],"By":[213],"systematically":[214],"tightening":[215],"coverage":[218],"intervals":[219],"biases,":[222],"provides":[224],"robust,":[226],"architecture-agnostic":[227],"solution":[228],"extraction":[232],"in":[233],"sensing.":[235]},"counts_by_year":[],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2026-04-23T00:00:00"}