{"id":"https://openalex.org/W2987544319","doi":"https://doi.org/10.1109/igarss.2019.8900199","title":"Attention based Residual Network for High-Resolution Remote Sensing Imagery Scene Classification","display_name":"Attention based Residual Network for High-Resolution Remote Sensing Imagery Scene Classification","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2987544319","doi":"https://doi.org/10.1109/igarss.2019.8900199","mag":"2987544319"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8900199","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8900199","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-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/A5058213496","display_name":"Runyu Fan","orcid":"https://orcid.org/0000-0002-5259-5670"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Runyu Fan","raw_affiliation_strings":["School of Computer Science, China University of Geosciences, Wuhan, P.R.China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, P.R.China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009116003","display_name":"Lizhe Wang","orcid":"https://orcid.org/0000-0003-2766-0845"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lizhe Wang","raw_affiliation_strings":["School of Computer Science, China University of Geosciences, Wuhan, P.R.China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, P.R.China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053931437","display_name":"Ruyi Feng","orcid":"https://orcid.org/0000-0002-5709-690X"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruyi Feng","raw_affiliation_strings":["School of Computer Science, China University of Geosciences, Wuhan, P.R.China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, P.R.China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112495326","display_name":"Yingqian Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingqian Zhu","raw_affiliation_strings":["School of Computer Science, China University of Geosciences, Wuhan, P.R.China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, P.R.China","institution_ids":["https://openalex.org/I3124059619"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5058213496"],"corresponding_institution_ids":["https://openalex.org/I3124059619"],"apc_list":null,"apc_paid":null,"fwci":3.5709,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.93426444,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1346","last_page":"1349"},"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":0.9965000152587891,"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":0.98089998960495,"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":0.7914894819259644},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.6899922490119934},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.6625762581825256},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6265567541122437},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5924349427223206},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.591051459312439},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5756944417953491},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5661011934280396},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4837186634540558},{"id":"https://openalex.org/keywords/remote-sensing-application","display_name":"Remote sensing application","score":0.46046942472457886},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.44141197204589844},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3649623394012451},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3230592608451843},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.27468565106391907},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.16578149795532227},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10855913162231445}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7914894819259644},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6899922490119934},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.6625762581825256},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6265567541122437},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5924349427223206},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.591051459312439},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5756944417953491},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5661011934280396},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4837186634540558},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.46046942472457886},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.44141197204589844},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3649623394012451},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3230592608451843},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.27468565106391907},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.16578149795532227},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10855913162231445},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8900199","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8900199","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","score":0.550000011920929,"id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1514535095","https://openalex.org/W1980038761","https://openalex.org/W2001123951","https://openalex.org/W2098676252","https://openalex.org/W2108598243","https://openalex.org/W2163605009","https://openalex.org/W2190186811","https://openalex.org/W2194775991","https://openalex.org/W2291068538","https://openalex.org/W2515866431","https://openalex.org/W2538244214","https://openalex.org/W2548390752","https://openalex.org/W2620429297","https://openalex.org/W2739323338","https://openalex.org/W2744582969","https://openalex.org/W2767814567","https://openalex.org/W2885765965","https://openalex.org/W2900579266","https://openalex.org/W2963495494","https://openalex.org/W3105577662","https://openalex.org/W4230714977","https://openalex.org/W6630875275","https://openalex.org/W6684191040","https://openalex.org/W6687157873"],"related_works":["https://openalex.org/W2952813363","https://openalex.org/W4378678253","https://openalex.org/W2911497689","https://openalex.org/W4360783045","https://openalex.org/W2770149305","https://openalex.org/W2972076240","https://openalex.org/W3167930666","https://openalex.org/W3014952856","https://openalex.org/W2964843961","https://openalex.org/W3010730661"],"abstract_inverted_index":{"Remote":[0,23],"sensing":[1,20,24,49,57,69,76,105,153],"image":[2,21,188],"scene":[3,40,71,107,155],"classification,":[4,72],"which":[5,46,73,90],"aims":[6],"to":[7,136,142,183,194],"identify":[8],"the":[9,131,134,163,187,192,201,209,215,222,228],"types":[10],"of":[11,29,130,186],"land":[12],"cover,":[13],"is":[14,139,157],"a":[15,27,36,64,158,168],"fundamental":[16],"task":[17],"in":[18,103,151],"remote":[19,48,56,68,75,104,152],"analysis.":[22],"images":[25,58],"contain":[26,59],"variety":[28],"land-cover":[30,33],"objects.":[31],"These":[32,118],"objects":[34],"form":[35],"complex":[37],"and":[38,44,127,147,175,189,214],"diverse":[39],"through":[41,95],"spatial":[42],"combination":[43],"correlation,":[45],"makes":[47,74],"imagery":[50,70,77,106,154],"scenes":[51,78],"classification":[52,79,108,156,230],"difficult.":[53],"In":[54],"addition,":[55],"redundant":[60,128,149,197],"information":[61,126,129,146,150],"that":[62,171,221],"has":[63,191,226],"negative":[65],"impact":[66],"on":[67,111,144,208],"rather":[80],"challenging.":[81],"Recently,":[82],"there":[83],"are":[84],"many":[85],"deep":[86],"learning":[87],"based":[88],"methods,":[89],"have":[91],"achieved":[92,227],"remarkable":[93],"performance":[94],"an":[96],"end-to-end":[97],"supervised":[98],"training":[99,112],"process.":[100],"Existing":[101],"advances":[102],"mainly":[109],"focus":[110,143],"multi-layer":[113],"convolutional":[114],"neural":[115],"networks":[116],"(CNNs).":[117],"CNNs":[119],"do":[120],"not":[121],"explicitly":[122],"distinguish":[123],"between":[124],"key":[125,145,184],"image.":[132],"Therefore,":[133],"ability":[135,193],"extract":[137],"features":[138],"limited.":[140],"How":[141],"ignore":[148,196],"valuable":[159],"problem.":[160],"Inspired":[161],"by":[162],"attention":[164,176,224],"mechanism,":[165],"we":[166],"propose":[167],"CNN-based":[169],"network":[170],"combines":[172],"residual":[173],"units":[174],"mechanism.":[177],"It":[178],"automatically":[179],"assigns":[180],"large":[181],"weights":[182],"areas":[185],"thus":[190],"adaptively":[195],"information.":[198],"We":[199],"evaluated":[200],"proposed":[202,223],"approach":[203],"with":[204],"some":[205],"state-of-the-art":[206],"methods":[207],"UC":[210],"Merced":[211],"Land-Use":[212],"dataset":[213],"NWPU-RESISC45":[216],"dataset.":[217],"Experimental":[218],"results":[219],"show":[220],"model":[225],"best":[229],"performance.":[231]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}