{"id":"https://openalex.org/W4366449618","doi":"https://doi.org/10.1145/3584376.3584496","title":"Remote Sensing Scene Classification with Lightweight Model","display_name":"Remote Sensing Scene Classification with Lightweight Model","publication_year":2022,"publication_date":"2022-12-16","ids":{"openalex":"https://openalex.org/W4366449618","doi":"https://doi.org/10.1145/3584376.3584496"},"language":"en","primary_location":{"id":"doi:10.1145/3584376.3584496","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3584376.3584496","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3584376.3584496","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3584376.3584496","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007740807","display_name":"Wenze Zhang","orcid":"https://orcid.org/0000-0001-8813-4908"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Wenze Zhang","raw_affiliation_strings":["Mathematics Department, University College London, the United Kingdom"],"affiliations":[{"raw_affiliation_string":"Mathematics Department, University College London, the United Kingdom","institution_ids":["https://openalex.org/I45129253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5007740807"],"corresponding_institution_ids":["https://openalex.org/I45129253"],"apc_list":null,"apc_paid":null,"fwci":0.11,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.5009808,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"680","last_page":"685"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9995999932289124,"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":0.9995999932289124,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.989799976348877,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.98089998960495,"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/computer-science","display_name":"Computer science","score":0.849558413028717},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6022427678108215},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5619407892227173},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.5342580080032349},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.4577471613883972},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.44216859340667725},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43757539987564087},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.4206123948097229},{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.418471097946167},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4166017174720764},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.4110981822013855},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37211430072784424},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3412715792655945},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33239251375198364},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33002108335494995}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.849558413028717},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6022427678108215},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5619407892227173},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.5342580080032349},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.4577471613883972},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.44216859340667725},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43757539987564087},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4206123948097229},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.418471097946167},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4166017174720764},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.4110981822013855},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37211430072784424},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3412715792655945},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33239251375198364},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33002108335494995},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3584376.3584496","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3584376.3584496","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3584376.3584496","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3584376.3584496","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3584376.3584496","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3584376.3584496","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4000000059604645}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4366449618.pdf"},"referenced_works_count":5,"referenced_works":["https://openalex.org/W2015386604","https://openalex.org/W2248723555","https://openalex.org/W2794393839","https://openalex.org/W2919115771","https://openalex.org/W4247150125"],"related_works":["https://openalex.org/W4387327236","https://openalex.org/W2183488467","https://openalex.org/W1990237101","https://openalex.org/W4309907966","https://openalex.org/W2028462208","https://openalex.org/W4387896287","https://openalex.org/W4285337533","https://openalex.org/W2982831492","https://openalex.org/W3170838353","https://openalex.org/W2187490799"],"abstract_inverted_index":{"Remote":[0],"sensing":[1],"has":[2],"significantly":[3],"progressed":[4],"and":[5,22,64,75,98,113,136,166,188,203],"are":[6,54,59,175],"becoming":[7],"more":[8],"readily":[9],"available":[10,71],"than":[11],"ever.":[12],"There":[13],"is":[14,49,86,120,134,140,152,200],"a":[15,110,201],"growing":[16],"requirement":[17],"for":[18,61,210],"the":[19,44,47,68,76,79,115,118,138,143,147,150,157,164,196],"automatic":[20],"recognition":[21],"labelling":[23],"of":[24,46,70,78,106,149,159,168],"these":[25,91],"images":[26,73,85,108,151],"under":[27],"various":[28],"conditions.":[29],"Many":[30],"studies":[31],"have":[32],"applied":[33],"deep":[34],"learning":[35],"networks":[36,101,174],"like":[37],"CNN":[38,100,173],"to":[39,81,83,102,124,154],"satellite":[40,72,107],"image":[41],"recognition.":[42],"However,":[43],"cost":[45],"models":[48],"high,":[50],"which":[51],"means":[52],"they":[53],"inappropriate":[55],"when":[56],"computing":[57],"resources":[58],"limited,":[60],"instance,":[62],"satellites":[63],"mobile":[65],"devices.":[66],"Moreover,":[67],"quality":[69,148],"varies,":[74],"ability":[77],"network":[80,199],"adapt":[82],"low-quality":[84],"also":[87],"critical.":[88],"To":[89,162],"solve":[90],"problems,":[92],"this":[93,211],"paper":[94],"uses":[95],"mobile-net":[96,132,169,197],"V2":[97,133,198],"traditional":[99,172],"classify":[103],"21":[104],"kinds":[105],"from":[109,122],"public":[111],"dataset":[112,119],"compare":[114],"results.":[116],"Firstly,":[117],"increased":[121],"100":[123],"500":[125],"per":[126],"class":[127],"through":[128],"data":[129,179,182],"augmentation.":[130],"Secondly,":[131],"trained":[135],"then":[137],"performance":[139],"evaluated":[141],"using":[142],"test":[144,181],"set.":[145],"Additionally,":[146],"reduced":[153],"figure":[155],"out":[156],"influence":[158],"each":[160],"model.":[161],"verify":[163],"effectiveness":[165],"accuracy":[167,209],"V2,":[170],"several":[171],"compared":[176],"with":[177,207],"validation":[178],"accuracy,":[180,183],"inference":[184],"time,":[185],"peak":[186],"RAM,":[187],"flash":[189],"usage.":[190],"The":[191],"experimental":[192],"results":[193],"show":[194],"that":[195],"low-cost":[202],"well":[204],"adaptability":[205],"model":[206],"high":[208],"multiclassification":[212],"work.":[213]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}