{"id":"https://openalex.org/W4377090936","doi":"https://doi.org/10.3233/jifs-222312","title":"A boundary optimization model of instance segmentation combined with wavelet transform on Buildings","display_name":"A boundary optimization model of instance segmentation combined with wavelet transform on Buildings","publication_year":2023,"publication_date":"2023-05-19","ids":{"openalex":"https://openalex.org/W4377090936","doi":"https://doi.org/10.3233/jifs-222312"},"language":"en","primary_location":{"id":"doi:10.3233/jifs-222312","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-222312","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent &amp; Fuzzy Systems","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/A5035542139","display_name":"Chenchen Shi","orcid":"https://orcid.org/0000-0002-8608-668X"},"institutions":[{"id":"https://openalex.org/I62853816","display_name":"Beijing University of Civil Engineering and Architecture","ror":"https://ror.org/02yj0p855","country_code":"CN","type":"education","lineage":["https://openalex.org/I62853816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenchen Shi","raw_affiliation_strings":["Science School, Beijing University of Civil Engineering and Architecture, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Science School, Beijing University of Civil Engineering and Architecture, Beijing, China","institution_ids":["https://openalex.org/I62853816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025961301","display_name":"Changlun Zhang","orcid":"https://orcid.org/0000-0002-1453-0286"},"institutions":[{"id":"https://openalex.org/I62853816","display_name":"Beijing University of Civil Engineering and Architecture","ror":"https://ror.org/02yj0p855","country_code":"CN","type":"education","lineage":["https://openalex.org/I62853816"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Changlun Zhang","raw_affiliation_strings":["Science School, Beijing University of Civil Engineering and Architecture, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Science School, Beijing University of Civil Engineering and Architecture, Beijing, China","institution_ids":["https://openalex.org/I62853816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109705640","display_name":"Lulu Deng","orcid":null},"institutions":[{"id":"https://openalex.org/I62853816","display_name":"Beijing University of Civil Engineering and Architecture","ror":"https://ror.org/02yj0p855","country_code":"CN","type":"education","lineage":["https://openalex.org/I62853816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lulu Deng","raw_affiliation_strings":["Science School, Beijing University of Civil Engineering and Architecture, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Science School, Beijing University of Civil Engineering and Architecture, Beijing, China","institution_ids":["https://openalex.org/I62853816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100777428","display_name":"Qiang He","orcid":"https://orcid.org/0000-0001-8485-4326"},"institutions":[{"id":"https://openalex.org/I62853816","display_name":"Beijing University of Civil Engineering and Architecture","ror":"https://ror.org/02yj0p855","country_code":"CN","type":"education","lineage":["https://openalex.org/I62853816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang He","raw_affiliation_strings":["Science School, Beijing University of Civil Engineering and Architecture, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Science School, Beijing University of Civil Engineering and Architecture, Beijing, China","institution_ids":["https://openalex.org/I62853816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050599074","display_name":"Hengyou Wang","orcid":"https://orcid.org/0000-0001-6693-0161"},"institutions":[{"id":"https://openalex.org/I62853816","display_name":"Beijing University of Civil Engineering and Architecture","ror":"https://ror.org/02yj0p855","country_code":"CN","type":"education","lineage":["https://openalex.org/I62853816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hengyou Wang","raw_affiliation_strings":["Science School, Beijing University of Civil Engineering and Architecture, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Science School, Beijing University of Civil Engineering and Architecture, Beijing, China","institution_ids":["https://openalex.org/I62853816"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040034589","display_name":"Lianzhi Huo","orcid":"https://orcid.org/0000-0001-6705-6453"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210137199","display_name":"Aerospace Information Research Institute","ror":"https://ror.org/0419fj215","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lianzhi Huo","raw_affiliation_strings":["Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China","E-mail:"],"affiliations":[{"raw_affiliation_string":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210137199","https://openalex.org/I19820366"]},{"raw_affiliation_string":"E-mail:","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5025961301"],"corresponding_institution_ids":["https://openalex.org/I62853816"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06971933,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"45","issue":"2","first_page":"1909","last_page":"1922"},"is_retracted":true,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9970999956130981,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9970999956130981,"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"}},{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9894999861717224,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7465735077857971},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7260957360267639},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6663241982460022},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5443460941314697},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.4867744445800781},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.43627768754959106},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4339447021484375},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.4206407368183136},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4158553183078766},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.3961377739906311},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1428208351135254}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7465735077857971},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7260957360267639},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6663241982460022},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5443460941314697},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.4867744445800781},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.43627768754959106},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4339447021484375},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.4206407368183136},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4158553183078766},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.3961377739906311},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1428208351135254},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/jifs-222312","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-222312","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent &amp; Fuzzy Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.5400000214576721,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2080518886","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2395611524","https://openalex.org/W2529656272","https://openalex.org/W2551161082","https://openalex.org/W2565639579","https://openalex.org/W2609402060","https://openalex.org/W2806070179","https://openalex.org/W2883423620","https://openalex.org/W2908320224","https://openalex.org/W2955425717","https://openalex.org/W2963111219","https://openalex.org/W2963570197","https://openalex.org/W2964241181","https://openalex.org/W2964309882","https://openalex.org/W2993182889","https://openalex.org/W3034428102","https://openalex.org/W3034826836","https://openalex.org/W3035358681","https://openalex.org/W3138211645","https://openalex.org/W3177165656","https://openalex.org/W3198064253","https://openalex.org/W4226479994","https://openalex.org/W4280626505","https://openalex.org/W6640054144","https://openalex.org/W6684191040","https://openalex.org/W6687483927","https://openalex.org/W6729575493","https://openalex.org/W6730903564","https://openalex.org/W6746085279","https://openalex.org/W6748481559","https://openalex.org/W6760232427","https://openalex.org/W6762718338","https://openalex.org/W6768489150","https://openalex.org/W6771194246","https://openalex.org/W6785792071","https://openalex.org/W6811259618"],"related_works":["https://openalex.org/W4315434538","https://openalex.org/W183670115","https://openalex.org/W1501179639","https://openalex.org/W3199035354","https://openalex.org/W2085792030","https://openalex.org/W1807354010","https://openalex.org/W3143644526","https://openalex.org/W1588899229","https://openalex.org/W2077021924","https://openalex.org/W2172291505"],"abstract_inverted_index":{"Data":[0],"driven":[1],"deep":[2,20],"learning":[3,21,138,150],"methods":[4],"have":[5],"become":[6],"the":[7,38,42,53,56,91,98,108,114,119,123,127,133,137,140,146,152,156,161,167,173,179,187,196,202,231,244,259,263],"mainstream":[8,58],"method":[9],"of":[10,34,44,55,70,102,113,132,139,145,151,169,178,205,266],"building":[11,82],"extraction":[12,204],"from":[13,30],"remote":[14,31],"sensing":[15,32],"images.":[16],"In":[17],"this":[18,74],"paper,":[19],"algorithm":[22,61,80,105,192,211,249],"is":[23,95,182,193],"used":[24],"to":[25,106,159,185,201],"classify":[26],"and":[27,67,110,142,175,199,217,226,234,270,275],"extract":[28,107,278],"buildings":[29],"images":[33],"rural":[35],"areas":[36],"around":[37],"Great":[39],"Wall":[40],"in":[41,64,117],"suburbs":[43],"Beijing":[45],"captured":[46],"by":[47,129,224],"unmanned":[48],"aerial":[49],"vehicles.":[50],"Aiming":[51],"at":[52],"shortcomings":[54],"current":[57],"instance":[59,71,83,245],"segmentation":[60,84,99,157,215,246,260],"Mask":[62,103,209,232,236],"R-CNN":[63,104,210,233,237],"feature":[65],"fusion":[66],"poor":[68,264],"prediction":[69,131],"mask":[72,134,181,267],"boundaries,":[73],"paper":[75],"proposes":[76],"a":[77],"boundary":[78,124,162],"optimization":[79,248],"for":[81],"based":[85],"on":[86,195,258],"discrete":[87,92,170],"wavelet":[88,93,171,252],"transform.":[89],"Firstly,":[90],"transform":[94,253],"introduced":[96],"into":[97,136],"task":[100],"branch":[101],"low-frequency":[109,141,174],"high-frequency":[111,120,143,153,176],"information":[112,121,144,154,177],"real":[115,147],"mask,":[116],"which":[118,221],"includes":[122],"information.":[125],"Secondly,":[126],"pixel":[128,130],"turns":[135],"mask.":[148,189],"The":[149,190,207,239],"helps":[155],"network":[158],"learn":[160],"features":[163],"better.":[164],"Finally,":[165],"using":[166],"reversibility":[168],"transform,":[172],"learned":[180],"inversely":[183],"transformed":[184],"reconstruct":[186],"final":[188],"improved":[191,223,262],"evaluated":[194],"dataset":[197],"COCO,":[198],"applied":[200],"automatic":[203],"buildings.":[206,280],"DWT":[208],"model":[212],"achieved":[213,255,271],"70.2%":[214],"accuracy":[216],"71.4%":[218],"detection":[219,269,273],"accuracy,":[220,274],"were":[222],"1%":[225],"0.7%":[227],"respectively":[228],"compared":[229],"with":[230,251],"Cascade":[235],"models.":[238],"experimental":[240],"results":[241,257],"show":[242],"that":[243],"edge":[247,268],"combined":[250],"has":[254],"better":[256],"boundary,":[261],"effect":[265],"higher":[272],"can":[276],"accurately":[277],"village":[279]},"counts_by_year":[],"updated_date":"2026-01-30T23:17:42.513302","created_date":"2025-10-10T00:00:00"}
