{"id":"https://openalex.org/W2970065770","doi":"https://doi.org/10.1109/lgrs.2019.2934341","title":"An Improved Pretraining Strategy-Based Scene Classification With Deep Learning","display_name":"An Improved Pretraining Strategy-Based Scene Classification With Deep Learning","publication_year":2019,"publication_date":"2019-08-27","ids":{"openalex":"https://openalex.org/W2970065770","doi":"https://doi.org/10.1109/lgrs.2019.2934341","mag":"2970065770"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2019.2934341","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2019.2934341","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":null,"display_name":"Zongli Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zongli Chen","raw_affiliation_strings":["Department of Land and Resources of Guizhou Province, Guiyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Land and Resources of Guizhou Province, Guiyang, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101447776","display_name":"Yiyue Wang","orcid":"https://orcid.org/0000-0002-7564-0067"},"institutions":[{"id":"https://openalex.org/I47689461","display_name":"Northeast Forestry University","ror":"https://ror.org/02yxnh564","country_code":"CN","type":"education","lineage":["https://openalex.org/I47689461"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiyue Wang","raw_affiliation_strings":["School of Computer Science, Northeast Forestry University, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northeast Forestry University, Harbin, China","institution_ids":["https://openalex.org/I47689461"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100750908","display_name":"Wei Han","orcid":"https://orcid.org/0000-0003-3882-1616"},"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":"Wei Han","raw_affiliation_strings":["Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China","School of Computer Science, China University of Geosciences, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-3882-1616","affiliations":[{"raw_affiliation_string":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, 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":["Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China","School of Computer Science, China University of Geosciences, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010242765","display_name":"Jia Chen","orcid":"https://orcid.org/0000-0002-9896-1656"},"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":"Jia Chen","raw_affiliation_strings":["Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China","School of Computer Science, China University of Geosciences, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-9896-1656","affiliations":[{"raw_affiliation_string":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7693,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.87003673,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"17","issue":"5","first_page":"844","last_page":"848"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987000226974487,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9929999709129333,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8228945732116699},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7397916316986084},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6682437658309937},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6032853722572327},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5379754900932312},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5302889943122864},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5021533966064453},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4803110957145691},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.43739187717437744},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42339515686035156},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4198707342147827},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33098459243774414}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8228945732116699},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7397916316986084},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6682437658309937},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6032853722572327},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5379754900932312},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5302889943122864},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5021533966064453},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4803110957145691},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.43739187717437744},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42339515686035156},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4198707342147827},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33098459243774414},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2019.2934341","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2019.2934341","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":[],"awards":[{"id":"https://openalex.org/G1596133006","display_name":null,"funder_award_id":"U1711266","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2846680537","display_name":null,"funder_award_id":"41571413","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6247301294","display_name":null,"funder_award_id":"41701429","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":31,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1980038761","https://openalex.org/W2097117768","https://openalex.org/W2098676252","https://openalex.org/W2108598243","https://openalex.org/W2149933564","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2303475025","https://openalex.org/W2515866431","https://openalex.org/W2531409750","https://openalex.org/W2549139847","https://openalex.org/W2592962403","https://openalex.org/W2617297862","https://openalex.org/W2626107033","https://openalex.org/W2752782242","https://openalex.org/W2771635595","https://openalex.org/W2782266583","https://openalex.org/W2789436332","https://openalex.org/W2920945696","https://openalex.org/W2963420686","https://openalex.org/W2991391304","https://openalex.org/W3103856189","https://openalex.org/W3105577662","https://openalex.org/W4299518610","https://openalex.org/W6637373629","https://openalex.org/W6682132143","https://openalex.org/W6684191040","https://openalex.org/W6738239618","https://openalex.org/W6756427901","https://openalex.org/W6760307695"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W2952813363","https://openalex.org/W4360783045"],"abstract_inverted_index":{"High-resolution":[0],"remote":[1,108],"sensing":[2,109],"(HRRS)":[3],"image":[4,74],"scene":[5,31,66,152,157],"classification":[6,67,153,158,190],"takes":[7],"an":[8,133,194,203],"important":[9],"role":[10],"in":[11,168,188],"many":[12],"applications":[13],"and":[14,50,76,98,124,165,228],"has":[15,220],"attracted":[16],"much":[17],"attention.":[18],"Recently,":[19],"notable":[20],"efforts":[21],"have":[22,77,231],"been":[23,79,221],"made":[24],"to":[25,81,83,181,201],"present":[26],"massive":[27],"methods":[28,35,68],"for":[29,72,107,206],"HRRS":[30,44,87,195,225],"classification,":[32],"wherein":[33],"deep-learning-based":[34,65],"demonstrate":[36],"remarkable":[37],"performance":[38],"compared":[39],"with":[40],"state-of-the-art":[41],"methods.":[42],"However,":[43],"images":[45],"contain":[46],"complex":[47],"contextual":[48],"relationships":[49],"large":[51],"differences":[52],"of":[53,86,94,113,121,131,185,235],"object":[54],"scale,":[55],"which":[56,89,211],"are":[57,69],"significantly":[58,90],"different":[59],"from":[60],"natural":[61,73],"images.":[62],"The":[63,118,155,217],"existing":[64],"originally":[70],"designed":[71],"processing":[75],"not":[78],"optimized":[80],"adapt":[82],"the":[84,92,95,111,114,129,141,170,183,189,208,214,233,236],"characteristics":[85],"images,":[88],"affects":[91],"efficiency":[93],"feature":[96,179],"extraction":[97],"recognition":[99],"accuracy.":[100],"In":[101,136,192],"addition,":[102,193],"when":[103],"designing":[104],"a":[105,146],"model":[106,115,205,209],"tasks,":[110],"pretraining":[112,122,215],"is":[116,160,166,199],"time-consuming.":[117],"enormous":[119],"amount":[120],"time":[123],"computation":[125],"resources":[126],"necessarily":[127],"increase":[128],"difficulty":[130],"producing":[132],"excellent":[134],"model.":[135],"this":[137],"letter,":[138],"focusing":[139],"on":[140],"problems":[142],"above,":[143],"we":[144],"proposed":[145,218,237],"new":[147],"convolutional":[148],"neural":[149],"network":[150],"(CNN)-based":[151],"method.":[154,238],"CNN-based":[156],"method":[159,219],"constructed":[161],"by":[162],"spatial-scale-aware":[163],"blocks":[164],"efficient":[167],"extracting":[169],"abundant":[171],"spatial":[172],"features,":[173],"but":[174],"can":[175],"also":[176],"adaptively":[177],"adjust":[178],"responses":[180],"maximize":[182],"function":[184],"informative":[186],"features":[187],"results.":[191],"imagery-based":[196],"learning":[197],"strategy":[198],"utilized":[200],"obtain":[202],"initial":[204],"fine-tuning":[207],"parameters,":[210],"drastically":[212],"reduces":[213],"time.":[216],"demonstrated":[222],"using":[223],"two":[224],"data":[226],"sets,":[227],"experimental":[229],"results":[230],"proven":[232],"superiority":[234]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}