{"id":"https://openalex.org/W2945581949","doi":"https://doi.org/10.1109/tbdata.2019.2916880","title":"Mining Deep Semantic Representations for Scene Classification of High-Resolution Remote Sensing Imagery","display_name":"Mining Deep Semantic Representations for Scene Classification of High-Resolution Remote Sensing Imagery","publication_year":2019,"publication_date":"2019-05-14","ids":{"openalex":"https://openalex.org/W2945581949","doi":"https://doi.org/10.1109/tbdata.2019.2916880","mag":"2945581949"},"language":"en","primary_location":{"id":"doi:10.1109/tbdata.2019.2916880","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2019.2916880","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Big Data","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/A5101971382","display_name":"Fan Hu","orcid":"https://orcid.org/0000-0001-5876-0721"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]},{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Hu","raw_affiliation_strings":["Electronic Information School and the State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-5876-0721","affiliations":[{"raw_affiliation_string":"Electronic Information School and the State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I4210118728","https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073032922","display_name":"Gui-Song Xia","orcid":"https://orcid.org/0000-0001-7660-6090"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]},{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gui-Song Xia","raw_affiliation_strings":["State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-7660-6090","affiliations":[{"raw_affiliation_string":"State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I4210118728","https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069540177","display_name":"Wen Yang","orcid":"https://orcid.org/0000-0002-3263-8768"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]},{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Yang","raw_affiliation_strings":["Electronic Information School and the State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-3263-8768","affiliations":[{"raw_affiliation_string":"Electronic Information School and the State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I4210118728","https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100673818","display_name":"Liangpei Zhang","orcid":"https://orcid.org/0000-0001-6890-3650"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]},{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liangpei Zhang","raw_affiliation_strings":["State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-6890-3650","affiliations":[{"raw_affiliation_string":"State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I4210118728","https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.0079,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.91947991,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"6","issue":"3","first_page":"522","last_page":"536"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9987999796867371,"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":0.9987999796867371,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9984999895095825,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9868999719619751,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/probabilistic-latent-semantic-analysis","display_name":"Probabilistic latent semantic analysis","score":0.8707042336463928},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8570464849472046},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6773366928100586},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6696929335594177},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6529603004455566},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5806959867477417},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5763862133026123},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5209449529647827},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5048843026161194},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.47714492678642273},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4490507245063782},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4376535713672638},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.43117809295654297},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4140092134475708}],"concepts":[{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.8707042336463928},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8570464849472046},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6773366928100586},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6696929335594177},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6529603004455566},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5806959867477417},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5763862133026123},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5209449529647827},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5048843026161194},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.47714492678642273},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4490507245063782},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4376535713672638},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.43117809295654297},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4140092134475708},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tbdata.2019.2916880","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2019.2916880","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Big Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G2099902736","display_name":null,"funder_award_id":"61771350","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G348483345","display_name":null,"funder_award_id":"41820104006","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4511722211","display_name":null,"funder_award_id":"61871299","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4835544183","display_name":null,"funder_award_id":"2017M622519","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":86,"referenced_works":["https://openalex.org/W40885937","https://openalex.org/W1498436455","https://openalex.org/W1524680991","https://openalex.org/W1526295910","https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W1880262756","https://openalex.org/W1899185266","https://openalex.org/W1909952827","https://openalex.org/W1912954554","https://openalex.org/W1960777822","https://openalex.org/W1963985200","https://openalex.org/W1968591910","https://openalex.org/W1980038761","https://openalex.org/W2001123951","https://openalex.org/W2005368619","https://openalex.org/W2006603039","https://openalex.org/W2040648426","https://openalex.org/W2060994933","https://openalex.org/W2062118960","https://openalex.org/W2067178723","https://openalex.org/W2077689834","https://openalex.org/W2077706444","https://openalex.org/W2086866337","https://openalex.org/W2097117768","https://openalex.org/W2098676252","https://openalex.org/W2100495367","https://openalex.org/W2102605133","https://openalex.org/W2103658758","https://openalex.org/W2105032938","https://openalex.org/W2107034620","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2113354691","https://openalex.org/W2117539524","https://openalex.org/W2121915926","https://openalex.org/W2125574651","https://openalex.org/W2131846894","https://openalex.org/W2134380836","https://openalex.org/W2134731454","https://openalex.org/W2147672495","https://openalex.org/W2151103935","https://openalex.org/W2153635508","https://openalex.org/W2154301842","https://openalex.org/W2155541015","https://openalex.org/W2155893237","https://openalex.org/W2161381512","https://openalex.org/W2162762921","https://openalex.org/W2162915993","https://openalex.org/W2163352848","https://openalex.org/W2163605009","https://openalex.org/W2163922914","https://openalex.org/W2179352600","https://openalex.org/W2187089797","https://openalex.org/W2190186811","https://openalex.org/W2194600502","https://openalex.org/W2194775991","https://openalex.org/W2253590344","https://openalex.org/W2291068538","https://openalex.org/W2298672219","https://openalex.org/W2466984109","https://openalex.org/W2515866431","https://openalex.org/W2592962403","https://openalex.org/W2607558879","https://openalex.org/W2620179114","https://openalex.org/W2620429297","https://openalex.org/W2727875856","https://openalex.org/W2764034829","https://openalex.org/W2782522152","https://openalex.org/W2919115771","https://openalex.org/W2962835968","https://openalex.org/W2963173190","https://openalex.org/W2963542991","https://openalex.org/W2963996492","https://openalex.org/W3098722327","https://openalex.org/W3105577662","https://openalex.org/W4231510805","https://openalex.org/W4294375521","https://openalex.org/W6629368666","https://openalex.org/W6637373629","https://openalex.org/W6639619044","https://openalex.org/W6639927594","https://openalex.org/W6677651945","https://openalex.org/W6682778277","https://openalex.org/W6684191040","https://openalex.org/W6687157873"],"related_works":["https://openalex.org/W2921491680","https://openalex.org/W2082325506","https://openalex.org/W2784194212","https://openalex.org/W2982493961","https://openalex.org/W2251863249","https://openalex.org/W2132052677","https://openalex.org/W4291700620","https://openalex.org/W4391903805","https://openalex.org/W2087743880","https://openalex.org/W4389937858"],"abstract_inverted_index":{"Scene":[0],"classification":[1],"is":[2,229],"one":[3],"of":[4,11,29,33,66,161,185,231],"the":[5,22,44,63,67,87,96,110,144,151,158,182,191,196,204,219,226,238],"most":[6],"fundamental":[7],"task":[8],"in":[9,142,166],"interpretation":[10],"high-resolution":[12],"remote":[13],"sensing":[14],"(HRRS)":[15],"images.":[16],"Many":[17],"recent":[18],"works":[19],"show":[20],"that":[21,225],"probabilistic":[23,88],"topic":[24,49,72],"models":[25,50],"which":[26,60],"are":[27,148],"capable":[28,230],"mining":[30],"latent":[31,89],"semantics":[32],"images":[34],"can":[35],"be":[36],"effectively":[37],"applied":[38],"to":[39,56,81,118],"HRRS":[40,210],"scene":[41,211],"classification.":[42],"However,":[43],"existing":[45],"approaches":[46],"based":[47],"on":[48,109,207],"simply":[51],"utilize":[52],"low-level":[53],"hand-crafted":[54,111],"features":[55,69,85,137,147,155,171,193,236],"form":[57],"semantic":[58,68,84,90,120,125,131,146,235],"features,":[59,121],"severely":[61],"limit":[62],"representative":[64],"capability":[65],"derived":[70],"from":[71,138,157,181,237],"models.":[73],"To":[74],"alleviate":[75],"this":[76,78],"problem,":[77],"paper":[79],"propose":[80],"build":[82],"powerful":[83],"using":[86],"analysis":[91],"(pLSA)":[92],"model,":[93],"by":[94,134,150,195],"employing":[95],"pre-trained":[97,163,187],"deep":[98,124,130,239],"convolutional":[99,159],"neural":[100],"networks":[101],"(CNNs)":[102],"as":[103],"feature":[104],"extractors":[105],"rather":[106],"than":[107],"relying":[108],"features.":[112,241],"Specifically,":[113],"we":[114,168],"develop":[115],"two":[116,205,208],"methods":[117,206],"generate":[119],"called":[122],"multi-scale":[123,154],"representation":[126,132],"(MSDS)":[127],"and":[128,189,213],"multi-level":[129],"(MLDS),":[133],"extracting":[135],"CNN":[136,170,240],"different":[139,178],"layers:":[140],"(1)":[141],"MSDS,":[143],"final":[145],"learned":[149,194],"pLSA":[152,197,227],"with":[153],"extracted":[156],"layer":[160,184],"a":[162,186],"CNN;":[164],"(2)":[165],"MLDS,":[167],"extract":[169],"for":[172],"densely":[173],"sampled":[174],"image":[175],"patches":[176],"at":[177,198],"size":[179],"level":[180],"fully-connected":[183],"CNN,":[188],"concatenate":[190],"sematic":[192],"each":[199],"level.":[200],"We":[201],"comprehensively":[202],"evaluate":[203],"public":[209],"datasets,":[212],"achieve":[214],"significant":[215],"performance":[216],"improvement":[217],"over":[218],"state-of-the-art.":[220],"The":[221],"outstanding":[222],"results":[223],"demonstrate":[224],"model":[228],"discovering":[232],"considerably":[233],"discriminative":[234]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
