{"id":"https://openalex.org/W2912114399","doi":"https://doi.org/10.3390/rs11030227","title":"Fusion of Multiscale Convolutional Neural Networks for Building Extraction in Very High-Resolution Images","display_name":"Fusion of Multiscale Convolutional Neural Networks for Building Extraction in Very High-Resolution Images","publication_year":2019,"publication_date":"2019-01-22","ids":{"openalex":"https://openalex.org/W2912114399","doi":"https://doi.org/10.3390/rs11030227","mag":"2912114399"},"language":"en","primary_location":{"id":"doi:10.3390/rs11030227","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11030227","pdf_url":"https://www.mdpi.com/2072-4292/11/3/227/pdf?version=1549868670","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/11/3/227/pdf?version=1549868670","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053518414","display_name":"Genyun Sun","orcid":"https://orcid.org/0000-0002-2641-2615"},"institutions":[{"id":"https://openalex.org/I4210113896","display_name":"Qingdao National Laboratory for Marine Science and Technology","ror":"https://ror.org/026sv7t11","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210113896"]},{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Genyun Sun","raw_affiliation_strings":["Key Laboratory of Deep Oil and Gas, Qingdao 266580, China","Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China","School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Deep Oil and Gas, Qingdao 266580, China","institution_ids":[]},{"raw_affiliation_string":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China","institution_ids":["https://openalex.org/I4210113896"]},{"raw_affiliation_string":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010097851","display_name":"Hui Huang","orcid":"https://orcid.org/0000-0001-5503-9090"},"institutions":[{"id":"https://openalex.org/I4210113896","display_name":"Qingdao National Laboratory for Marine Science and Technology","ror":"https://ror.org/026sv7t11","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210113896"]},{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Huang","raw_affiliation_strings":["Key Laboratory of Deep Oil and Gas, Qingdao 266580, China","Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China","School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Deep Oil and Gas, Qingdao 266580, China","institution_ids":[]},{"raw_affiliation_string":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China","institution_ids":["https://openalex.org/I4210113896"]},{"raw_affiliation_string":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074984354","display_name":"Aizhu Zhang","orcid":"https://orcid.org/0000-0003-2226-8908"},"institutions":[{"id":"https://openalex.org/I4210113896","display_name":"Qingdao National Laboratory for Marine Science and Technology","ror":"https://ror.org/026sv7t11","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210113896"]},{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Aizhu Zhang","raw_affiliation_strings":["Key Laboratory of Deep Oil and Gas, Qingdao 266580, China","Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China","School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Deep Oil and Gas, Qingdao 266580, China","institution_ids":[]},{"raw_affiliation_string":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China","institution_ids":["https://openalex.org/I4210113896"]},{"raw_affiliation_string":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100376064","display_name":"Li Feng","orcid":"https://orcid.org/0000-0001-6128-3663"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Feng Li","raw_affiliation_strings":["School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China","Shandong Provincial Climate Center, Jinan 250000, China"],"affiliations":[{"raw_affiliation_string":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China","institution_ids":["https://openalex.org/I4210162190"]},{"raw_affiliation_string":"Shandong Provincial Climate Center, Jinan 250000, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075054710","display_name":"Huimin Zhao","orcid":"https://orcid.org/0000-0002-6877-2002"},"institutions":[{"id":"https://openalex.org/I4210122543","display_name":"Guangdong Polytechnic Normal University","ror":"https://ror.org/02pcb5m77","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210122543"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huimin Zhao","raw_affiliation_strings":["School of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou 510000, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou 510000, China","institution_ids":["https://openalex.org/I4210122543"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075063437","display_name":"Hang Fu","orcid":"https://orcid.org/0009-0007-5592-7697"},"institutions":[{"id":"https://openalex.org/I4210113896","display_name":"Qingdao National Laboratory for Marine Science and Technology","ror":"https://ror.org/026sv7t11","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210113896"]},{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hang Fu","raw_affiliation_strings":["Key Laboratory of Deep Oil and Gas, Qingdao 266580, China","Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China","School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Deep Oil and Gas, Qingdao 266580, China","institution_ids":[]},{"raw_affiliation_string":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China","institution_ids":["https://openalex.org/I4210113896"]},{"raw_affiliation_string":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China","institution_ids":["https://openalex.org/I4210162190"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5074984354","https://openalex.org/A5100376064"],"corresponding_institution_ids":["https://openalex.org/I4210113896","https://openalex.org/I4210162190"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":8.7026,"has_fulltext":true,"cited_by_count":77,"citation_normalized_percentile":{"value":0.97853492,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"11","issue":"3","first_page":"227","last_page":"227"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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":1.0,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9976999759674072,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9973999857902527,"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":0.801662266254425},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.793586254119873},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7240976095199585},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7196184396743774},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7047924995422363},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5442145466804504},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5242679119110107},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4607705771923065},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.43832284212112427},{"id":"https://openalex.org/keywords/decision-boundary","display_name":"Decision boundary","score":0.420685350894928}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.801662266254425},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.793586254119873},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7240976095199585},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7196184396743774},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7047924995422363},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5442145466804504},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5242679119110107},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4607705771923065},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.43832284212112427},{"id":"https://openalex.org/C42023084","wikidata":"https://www.wikidata.org/wiki/Q5249231","display_name":"Decision boundary","level":3,"score":0.420685350894928}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs11030227","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11030227","pdf_url":"https://www.mdpi.com/2072-4292/11/3/227/pdf?version=1549868670","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f6ebb89873ee4b8c88875917baa2d307","is_oa":true,"landing_page_url":"https://doaj.org/article/f6ebb89873ee4b8c88875917baa2d307","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 11, Iss 3, p 227 (2019)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/11/3/227/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs11030227","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 11; Issue 3; Pages: 227","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs11030227","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11030227","pdf_url":"https://www.mdpi.com/2072-4292/11/3/227/pdf?version=1549868670","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6000000238418579,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2376276132","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G2696157397","display_name":null,"funder_award_id":"ZR201","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G2862249462","display_name":null,"funder_award_id":"ZR2018BD007","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G360494493","display_name":null,"funder_award_id":"18CX05030A, 18CX02179A","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G4672554871","display_name":null,"funder_award_id":"18CX02179A","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G5955471608","display_name":null,"funder_award_id":"41801275","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6924028813","display_name":null,"funder_award_id":"ZR2018","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G7166667683","display_name":null,"funder_award_id":"18CX05030A","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320324174","display_name":"Natural Science Foundation of Shandong Province","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2912114399.pdf","grobid_xml":"https://content.openalex.org/works/W2912114399.grobid-xml"},"referenced_works_count":71,"referenced_works":["https://openalex.org/W73112891","https://openalex.org/W1771726589","https://openalex.org/W1932624639","https://openalex.org/W1973644502","https://openalex.org/W1974524700","https://openalex.org/W1976827068","https://openalex.org/W1999478155","https://openalex.org/W2000803298","https://openalex.org/W2022156723","https://openalex.org/W2026883296","https://openalex.org/W2061421991","https://openalex.org/W2064675550","https://openalex.org/W2065972554","https://openalex.org/W2066916495","https://openalex.org/W2067191022","https://openalex.org/W2076131212","https://openalex.org/W2078478672","https://openalex.org/W2087330236","https://openalex.org/W2094682449","https://openalex.org/W2104125540","https://openalex.org/W2109565719","https://openalex.org/W2112796928","https://openalex.org/W2112803241","https://openalex.org/W2118246710","https://openalex.org/W2118286367","https://openalex.org/W2120480077","https://openalex.org/W2121947440","https://openalex.org/W2124706543","https://openalex.org/W2134337515","https://openalex.org/W2136251662","https://openalex.org/W2136922672","https://openalex.org/W2137855675","https://openalex.org/W2150621701","https://openalex.org/W2155910279","https://openalex.org/W2157284958","https://openalex.org/W2157559031","https://openalex.org/W2163605009","https://openalex.org/W2164976328","https://openalex.org/W2167962735","https://openalex.org/W2253590344","https://openalex.org/W2267317359","https://openalex.org/W2300635092","https://openalex.org/W2329412872","https://openalex.org/W2412782625","https://openalex.org/W2570837606","https://openalex.org/W2585293115","https://openalex.org/W2598551616","https://openalex.org/W2623490820","https://openalex.org/W2648242067","https://openalex.org/W2752971420","https://openalex.org/W2753816389","https://openalex.org/W2770967191","https://openalex.org/W2774038444","https://openalex.org/W2777439179","https://openalex.org/W2787272945","https://openalex.org/W2787614951","https://openalex.org/W2794187036","https://openalex.org/W2795635230","https://openalex.org/W2800388963","https://openalex.org/W2810004461","https://openalex.org/W2887148931","https://openalex.org/W2890072312","https://openalex.org/W2891854043","https://openalex.org/W2901928882","https://openalex.org/W2962949934","https://openalex.org/W2963470893","https://openalex.org/W2963881378","https://openalex.org/W3104925044","https://openalex.org/W4256508898","https://openalex.org/W6674282598","https://openalex.org/W6732518894"],"related_works":["https://openalex.org/W1487808658","https://openalex.org/W2924161931","https://openalex.org/W2981213860","https://openalex.org/W2019469476","https://openalex.org/W2580636385","https://openalex.org/W103332046","https://openalex.org/W598997827","https://openalex.org/W4307307693","https://openalex.org/W3127069801","https://openalex.org/W4294023778"],"abstract_inverted_index":{"Extracting":[0],"buildings":[1,249],"from":[2],"very":[3],"high":[4],"resolution":[5],"(VHR)":[6],"images":[7,177],"has":[8],"attracted":[9],"much":[10],"attention":[11],"but":[12],"is":[13,62,184],"still":[14],"challenging":[15],"due":[16],"to":[17,54,66,89,101,123,153,174,200],"their":[18],"large":[19,56],"varieties":[20],"in":[21,35,42,229,246,250],"appearance":[22],"and":[23,32,39,80,151,221],"scale.":[24],"Convolutional":[25],"neural":[26],"networks":[27],"(CNNs)":[28],"have":[29,237],"shown":[30],"effective":[31],"superior":[33,240],"performance":[34,213,241],"automatically":[36],"learning":[37],"high-level":[38],"discriminative":[40],"features":[41,73,129,160],"extracting":[43,247],"buildings.":[44],"However,":[45],"the":[46,71,96,106,136,188,202,205,215,239,243],"fixed":[47],"receptive":[48],"fields":[49],"make":[50,86],"conventional":[51],"CNNs":[52],"insufficient":[53],"tolerate":[55],"scale":[57],"changes.":[58],"Multiscale":[59],"CNN":[60],"(MCNN)":[61],"a":[63,102,114,142],"promising":[64],"structure":[65,144],"meet":[67],"this":[68,110],"challenge.":[69],"Unfortunately,":[70],"multiscale":[72,155],"extracted":[74,107,134],"by":[75,135],"MCNN":[76,137,143],"are":[77,198],"always":[78],"stacked":[79],"fed":[81,166],"into":[82,167],"one":[83],"classifier,":[84],"which":[85],"it":[87],"difficult":[88],"recognize":[90],"objects":[91],"with":[92,145,231],"different":[93,131,146,162,168],"scales.":[94],"Besides,":[95],"repeated":[97],"sub-sampling":[98],"processes":[99],"lead":[100],"blurred":[103],"boundary":[104,203],"of":[105,127,148,204,242],"features.":[108,157],"In":[109],"study,":[111],"we":[112],"proposed":[113,244],"novel":[115],"parallel":[116],"support":[117,169],"vector":[118,170],"mechanism":[119],"(SVM)-based":[120],"fusion":[121,182],"strategy":[122,183],"take":[124],"full":[125],"use":[126],"deep":[128,156],"at":[130,161],"scales":[132,163],"as":[133],"structure.":[138],"We":[139],"firstly":[140],"designed":[141],"sizes":[147],"input":[149],"patches":[150],"kernels,":[152],"learn":[154],"After":[158],"that,":[159],"were":[164],"individually":[165],"machine":[171],"(SVM)":[172],"classifiers":[173],"produce":[175],"rule":[176],"for":[178,219],"pre-classification.":[179],"A":[180],"decision":[181],"then":[185],"applied":[186,199],"on":[187,192],"pre-classification":[189],"results":[190,207,236],"based":[191],"another":[193],"SVM":[194],"classifier.":[195],"Finally,":[196],"superpixels":[197],"refine":[201],"fused":[206],"using":[208],"region-based":[209],"maximum":[210],"voting.":[211],"For":[212],"evaluation,":[214],"well-known":[216],"International":[217],"Society":[218],"Photogrammetry":[220],"Remote":[222],"Sensing":[223],"(ISPRS)":[224],"Potsdam":[225],"dataset":[226],"was":[227],"used":[228],"comparison":[230],"several":[232],"state-of-the-art":[233],"algorithms.":[234],"Experimental":[235],"demonstrated":[238],"methodology":[245],"complex":[248],"urban":[251],"districts.":[252]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":19},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":5}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2019-02-21T00:00:00"}
