{"id":"https://openalex.org/W3026485923","doi":"https://doi.org/10.1109/lgrs.2020.2992929","title":"Spatial Information Considered Network for Scene Classification","display_name":"Spatial Information Considered Network for Scene Classification","publication_year":2020,"publication_date":"2020-05-18","ids":{"openalex":"https://openalex.org/W3026485923","doi":"https://doi.org/10.1109/lgrs.2020.2992929","mag":"3026485923"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2020.2992929","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.2992929","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/A5073640524","display_name":"Chao Tao","orcid":"https://orcid.org/0000-0003-0071-310X"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Tao","raw_affiliation_strings":["School of Geosciences and Info-Physics, Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0003-0071-310X","affiliations":[{"raw_affiliation_string":"School of Geosciences and Info-Physics, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006120418","display_name":"Weipeng Lu","orcid":"https://orcid.org/0000-0002-1026-1007"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weipeng Lu","raw_affiliation_strings":["School of Geosciences and Info-Physics, Central South University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Geosciences and Info-Physics, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002710782","display_name":"Ji Qi","orcid":"https://orcid.org/0000-0001-7948-579X"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ji Qi","raw_affiliation_strings":["School of Geosciences and Info-Physics, Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0001-7948-579X","affiliations":[{"raw_affiliation_string":"School of Geosciences and Info-Physics, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100743778","display_name":"Hao Wang","orcid":"https://orcid.org/0000-0002-3964-479X"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Wang","raw_affiliation_strings":["School of Geosciences and Info-Physics, Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-3964-479X","affiliations":[{"raw_affiliation_string":"School of Geosciences and Info-Physics, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.678,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.91724375,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"18","issue":"6","first_page":"984","last_page":"988"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9983000159263611,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9954000115394592,"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/computer-science","display_name":"Computer science","score":0.7975144982337952},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7328282594680786},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7225092649459839},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6905262470245361},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.5608779788017273},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5457732677459717},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5115386247634888},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5087524056434631},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.5030128359794617},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.49918341636657715},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.43159449100494385},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.42287325859069824},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.41407260298728943},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32795703411102295},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23414897918701172},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.16339638829231262},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11967340111732483}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7975144982337952},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7328282594680786},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7225092649459839},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6905262470245361},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.5608779788017273},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5457732677459717},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5115386247634888},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5087524056434631},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5030128359794617},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.49918341636657715},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.43159449100494385},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.42287325859069824},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.41407260298728943},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32795703411102295},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23414897918701172},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.16339638829231262},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11967340111732483},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2020.2992929","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.2992929","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":[{"score":0.7599999904632568,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G2090819383","display_name":"\u9ad8\u5206\u8fa8\u7387\u9065\u611f\u5f71\u50cf\"\u573a\u666f-\u76ee\u6807\"\u534f\u540c\u7406\u89e3\u7406\u8bba\u4e0e\u65b9\u6cd5\u7814\u7a76","funder_award_id":"41771458","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4284791750","display_name":null,"funder_award_id":"41301453","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1664573881","https://openalex.org/W1912954554","https://openalex.org/W1980038761","https://openalex.org/W1990895816","https://openalex.org/W2098676252","https://openalex.org/W2151103935","https://openalex.org/W2161969291","https://openalex.org/W2194775991","https://openalex.org/W2347115704","https://openalex.org/W2515866431","https://openalex.org/W2531409750","https://openalex.org/W2551397753","https://openalex.org/W2592962403","https://openalex.org/W2626107033","https://openalex.org/W2783165089","https://openalex.org/W2889192935","https://openalex.org/W2890732922","https://openalex.org/W2897086142","https://openalex.org/W2899198451","https://openalex.org/W2945385604","https://openalex.org/W2948656358","https://openalex.org/W2982628450","https://openalex.org/W3103856189","https://openalex.org/W3105577662","https://openalex.org/W3106090851","https://openalex.org/W6637116368"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W1590307681","https://openalex.org/W4312814274","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703","https://openalex.org/W2761785940"],"abstract_inverted_index":{"Remote":[0],"sensing":[1],"image":[2],"(RSI)":[3],"scene":[4,133,172],"classification":[5,173],"(RSISC)":[6],"is":[7],"a":[8,72,116,155],"fundamental":[9],"problem":[10],"for":[11],"understanding":[12],"high-resolution":[13],"RSIs.":[14],"More":[15],"recently,":[16],"deep":[17,35],"learning":[18,36],"methods,":[19],"especially":[20],"convolutional":[21],"neural":[22,87],"networks":[23],"(CNNs),":[24],"and":[25,56,85,96],"large":[26],"data":[27,140,145,176],"sets":[28],"have":[29],"greatly":[30],"promoted":[31],"the":[32,41,46,53,89,103,107,111,127,149,163],"RSISC.":[33],"However,":[34],"methods":[37,170],"rely":[38],"heavily":[39],"on":[40],"visual":[42,113],"features":[43,114,123],"extracted":[44],"from":[45,49],"patches":[47],"cropped":[48],"original":[50],"RSIs,":[51],"so":[52],"intraclass":[54],"diversity":[55],"interclass":[57],"similarity":[58],"are":[59,119],"two":[60],"big":[61],"challenges.":[62],"To":[63],"address":[64],"these":[65],"problems,":[66],"in":[67,154,171],"this":[68],"letter,":[69],"we":[70,136],"propose":[71],"spatial":[73,98,125,150],"information":[74,100,151],"considered":[75],"model":[76],"to":[77,101,147],"learn":[78],"more":[79,131],"discriminative":[80],"features.":[81,109],"By":[82],"combining":[83],"CNN":[84],"recurrent":[86],"network,":[88],"proposed":[90,128,164],"method":[91,129,165],"can":[92],"exploit":[93],"both":[94],"local":[95],"long-range":[97],"relation":[99],"enhance":[102],"representational":[104],"ability":[105],"of":[106,115,158],"learned":[108],"As":[110],"initial":[112],"single":[117],"patch":[118],"transformed":[120],"into":[121],"higher-level":[122],"with":[124],"information,":[126],"achieves":[130],"accurate":[132],"classification.":[134],"Besides,":[135],"present":[137],"an":[138],"RSISC":[139],"set":[141,146],"named":[142],"as":[143],"CSU-RSISC10":[144,175],"preserve":[148],"between":[152],"scenes":[153],"new":[156],"way":[157],"organization.":[159],"Experiments":[160],"demonstrate":[161],"that":[162],"outperforms":[166],"other":[167],"three":[168],"state-of-the-art":[169],"using":[174],"set.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
