{"id":"https://openalex.org/W2808436940","doi":"https://doi.org/10.1109/lgrs.2018.2839092","title":"Enhanced Fusion of Deep Neural Networks for Classification of Benchmark High-Resolution Image Data Sets","display_name":"Enhanced Fusion of Deep Neural Networks for Classification of Benchmark High-Resolution Image Data Sets","publication_year":2018,"publication_date":"2018-06-14","ids":{"openalex":"https://openalex.org/W2808436940","doi":"https://doi.org/10.1109/lgrs.2018.2839092","mag":"2808436940"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2018.2839092","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2018.2839092","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5051712346","display_name":"Grant J. Scott","orcid":"https://orcid.org/0000-0001-5870-9387"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Grant J. Scott","raw_affiliation_strings":["Center for Geospatial Intelligence, University of Missouri, Columbia, MO, USA"],"raw_orcid":"https://orcid.org/0000-0001-5870-9387","affiliations":[{"raw_affiliation_string":"Center for Geospatial Intelligence, University of Missouri, Columbia, MO, USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010675620","display_name":"Kyle C. Hagan","orcid":null},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kyle C. Hagan","raw_affiliation_strings":["Center for Geospatial Intelligence, University of Missouri, Columbia, MO, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Geospatial Intelligence, University of Missouri, Columbia, MO, USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110672954","display_name":"Richard A. Marcum","orcid":null},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Richard A. Marcum","raw_affiliation_strings":["Center for Geospatial Intelligence, University of Missouri, Columbia, MO, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Geospatial Intelligence, University of Missouri, Columbia, MO, USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009311723","display_name":"J. Alex Hurt","orcid":"https://orcid.org/0000-0002-7234-1301"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James Alex Hurt","raw_affiliation_strings":["Center for Geospatial Intelligence, University of Missouri, Columbia, MO, USA"],"raw_orcid":"https://orcid.org/0000-0002-7234-1301","affiliations":[{"raw_affiliation_string":"Center for Geospatial Intelligence, University of Missouri, Columbia, MO, USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076164452","display_name":"Derek T. Anderson","orcid":"https://orcid.org/0000-0003-4909-029X"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Derek T. Anderson","raw_affiliation_strings":["Center for Geospatial Intelligence, University of Missouri, Columbia, MO, USA"],"raw_orcid":"https://orcid.org/0000-0003-4909-029X","affiliations":[{"raw_affiliation_string":"Center for Geospatial Intelligence, University of Missouri, Columbia, MO, USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079391589","display_name":"Curt H. Davis","orcid":"https://orcid.org/0000-0002-5781-0931"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Curt H. Davis","raw_affiliation_strings":["Center for Geospatial Intelligence, University of Missouri, Columbia, MO, USA"],"raw_orcid":"https://orcid.org/0000-0002-5781-0931","affiliations":[{"raw_affiliation_string":"Center for Geospatial Intelligence, University of Missouri, Columbia, MO, USA","institution_ids":["https://openalex.org/I76835614"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76835614"],"apc_list":null,"apc_paid":null,"fwci":9.3985,"has_fulltext":false,"cited_by_count":81,"citation_normalized_percentile":{"value":0.98143824,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"15","issue":"9","first_page":"1451","last_page":"1455"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9969000220298767,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9940999746322632,"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/benchmark","display_name":"Benchmark (surveying)","score":0.8383759260177612},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7143422365188599},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6887375116348267},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6597179770469666},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.594255805015564},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5555002093315125},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5028230547904968},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.48655787110328674},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4737551808357239},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4566265046596527},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.43771013617515564},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43232786655426025},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.42052942514419556},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4119235873222351},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.35517972707748413},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35362762212753296},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.08663886785507202},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0638282299041748}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8383759260177612},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7143422365188599},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6887375116348267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6597179770469666},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.594255805015564},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5555002093315125},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5028230547904968},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.48655787110328674},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4737551808357239},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4566265046596527},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.43771013617515564},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43232786655426025},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.42052942514419556},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4119235873222351},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.35517972707748413},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35362762212753296},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.08663886785507202},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0638282299041748},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2018.2839092","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2018.2839092","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.6600000262260437,"display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1912954554","https://openalex.org/W1980038761","https://openalex.org/W1984859563","https://openalex.org/W2020912318","https://openalex.org/W2097117768","https://openalex.org/W2155893237","https://openalex.org/W2194775991","https://openalex.org/W2515866431","https://openalex.org/W2531409750","https://openalex.org/W2588561483","https://openalex.org/W2592962403","https://openalex.org/W2617297862","https://openalex.org/W2623978803","https://openalex.org/W2626107033","https://openalex.org/W2791948218","https://openalex.org/W2801277493","https://openalex.org/W2963446712","https://openalex.org/W2964350391","https://openalex.org/W3103856189","https://openalex.org/W3105577662","https://openalex.org/W4248710273","https://openalex.org/W6694260854"],"related_works":["https://openalex.org/W2386346147","https://openalex.org/W2016474688","https://openalex.org/W2392191343","https://openalex.org/W2357430536","https://openalex.org/W3088219559","https://openalex.org/W2388838478","https://openalex.org/W2992915961","https://openalex.org/W2548950561","https://openalex.org/W1541747552","https://openalex.org/W2043913960"],"abstract_inverted_index":{"Accurate":[0],"land":[1,34],"cover":[2,35],"classification":[3,36,72,103],"and":[4,37,60,70,80],"detection":[5,39],"of":[6,54],"objects":[7],"in":[8,96,102],"high-resolution":[9],"electro-optical":[10],"remote":[11],"sensing":[12],"imagery":[13],"(RSI)":[14],"have":[15,27],"long":[16],"been":[17,28],"a":[18,52,90],"challenging":[19],"task.":[20],"Recently,":[21],"important":[22],"new":[23],"benchmark":[24,48],"data":[25,49,82,108],"sets":[26,50],"released":[29],"which":[30],"are":[31],"suitable":[32],"for":[33,46],"object":[38],"research.":[40],"Here,":[41],"we":[42],"present":[43],"state-of-the-art":[44],"results":[45],"four":[47],"using":[51,85],"variety":[53],"deep":[55],"convolutional":[56],"neural":[57],"networks":[58],"(DCNN)":[59],"multiple":[61],"network":[62],"fusion":[63],"techniques.":[64],"We":[65],"achieve":[66],"99.70%,":[67],"99.66%,":[68],"97.74%,":[69],"97.30%":[71],"accuracies":[73],"on":[74],"the":[75,86,115],"PatternNet,":[76],"RSI-CB256,":[77],"aerial":[78],"image,":[79],"RESISC-45":[81],"sets,":[83],"respectively,":[84],"Choquet":[87],"integral":[88],"with":[89,114],"novel":[91],"data-driven":[92],"optimization":[93,110],"method":[94],"presented":[95],"this":[97,107],"letter.":[98],"The":[99],"relative":[100],"reduction":[101],"errors":[104],"achieved":[105],"by":[106],"driven":[109],"is":[111],"25%-45%":[112],"compared":[113],"single":[116],"best":[117],"DCNN":[118],"results.":[119]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":19},{"year":2019,"cited_by_count":12},{"year":2018,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}