{"id":"https://openalex.org/W4364322110","doi":"https://doi.org/10.1109/tgrs.2023.3265669","title":"EFM-Net: An Essential Feature Mining Network for Target Fine-Grained Classification in Optical Remote Sensing Images","display_name":"EFM-Net: An Essential Feature Mining Network for Target Fine-Grained Classification in Optical Remote Sensing Images","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4364322110","doi":"https://doi.org/10.1109/tgrs.2023.3265669"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2023.3265669","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3265669","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","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/A5053663653","display_name":"Yonghao Yi","orcid":"https://orcid.org/0000-0001-7994-9283"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yonghao Yi","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7994-9283","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008579555","display_name":"Yanan You","orcid":"https://orcid.org/0000-0001-6473-9187"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanan You","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6473-9187","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100695741","display_name":"Chao Li","orcid":"https://orcid.org/0000-0003-1492-5410"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Li","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1492-5410","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101778892","display_name":"Wenli Zhou","orcid":"https://orcid.org/0000-0002-6786-1257"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenli Zhou","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":3.2972,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.9389612,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"61","issue":null,"first_page":"1","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9991000294685364,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9991000294685364,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9991000294685364,"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":0.9990000128746033,"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":0.8478296995162964},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7087918519973755},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.656822681427002},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6345919370651245},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6024389863014221},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.581169843673706},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5414872169494629},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5112488269805908},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5080106258392334},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3720581531524658},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.26503774523735046}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8478296995162964},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7087918519973755},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.656822681427002},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6345919370651245},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6024389863014221},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.581169843673706},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5414872169494629},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5112488269805908},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5080106258392334},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3720581531524658},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.26503774523735046},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/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/tgrs.2023.3265669","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3265669","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6499999761581421}],"awards":[{"id":"https://openalex.org/G6445369638","display_name":null,"funder_award_id":"62101060","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G74333972","display_name":null,"funder_award_id":"4214058","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":68,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2104657103","https://openalex.org/W2163605009","https://openalex.org/W2183341477","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2410591237","https://openalex.org/W2512351403","https://openalex.org/W2549139847","https://openalex.org/W2554320282","https://openalex.org/W2565639579","https://openalex.org/W2594177559","https://openalex.org/W2737725206","https://openalex.org/W2752782242","https://openalex.org/W2891951760","https://openalex.org/W2895643041","https://openalex.org/W2911814348","https://openalex.org/W2915963825","https://openalex.org/W2921708311","https://openalex.org/W2961018736","https://openalex.org/W2962749812","https://openalex.org/W2962858109","https://openalex.org/W2963066927","https://openalex.org/W2963383024","https://openalex.org/W2963407932","https://openalex.org/W2963446712","https://openalex.org/W2964350570","https://openalex.org/W2998619563","https://openalex.org/W3003180752","https://openalex.org/W3013723079","https://openalex.org/W3016719260","https://openalex.org/W3034907931","https://openalex.org/W3035367622","https://openalex.org/W3095319281","https://openalex.org/W3101126838","https://openalex.org/W3111811104","https://openalex.org/W3116516381","https://openalex.org/W3118843071","https://openalex.org/W3133008479","https://openalex.org/W3138516171","https://openalex.org/W3161530673","https://openalex.org/W3165948017","https://openalex.org/W3178389175","https://openalex.org/W3200799048","https://openalex.org/W3208364069","https://openalex.org/W3210464403","https://openalex.org/W3212176979","https://openalex.org/W3212813052","https://openalex.org/W3213833596","https://openalex.org/W4205365435","https://openalex.org/W4221070596","https://openalex.org/W4225467463","https://openalex.org/W4226015333","https://openalex.org/W4226456028","https://openalex.org/W4285300322","https://openalex.org/W4304084267","https://openalex.org/W4312443924","https://openalex.org/W4312546407","https://openalex.org/W4312828812","https://openalex.org/W6637373629","https://openalex.org/W6638444622","https://openalex.org/W6684191040","https://openalex.org/W6786919053","https://openalex.org/W6795598648","https://openalex.org/W6795772409","https://openalex.org/W6798264847","https://openalex.org/W6802619180","https://openalex.org/W6803421043"],"related_works":["https://openalex.org/W2911497689","https://openalex.org/W2761785940","https://openalex.org/W2952813363","https://openalex.org/W4360783045","https://openalex.org/W2129933262","https://openalex.org/W2963346891","https://openalex.org/W2770149305","https://openalex.org/W3167930666","https://openalex.org/W3014952856","https://openalex.org/W3010730661"],"abstract_inverted_index":{"Target":[0],"fine-grained":[1,25,47,147],"classification":[2,26,49,148],"has":[3,14],"been":[4],"the":[5,24,31,36,55,95,111,115,119,136,163],"research":[6],"hotspot":[7],"in":[8,18,65,129,150,176],"remote":[9,66,151],"sensing":[10,67],"image":[11,48],"interpretation,":[12],"which":[13,60,98],"received":[15],"general":[16],"attention":[17],"application":[19],"fields.":[20],"One":[21],"challenge":[22],"of":[23,46,139],"task":[27],"is":[28,92,99],"to":[29,85,93,102],"learn":[30],"most":[32],"discriminative":[33],"feature":[34,56,76,97,117,138],"using":[35],"deep":[37],"convolutional":[38],"neural":[39],"network":[40,78],"(DCNN).":[41],"At":[42],"present,":[43],"many":[44],"works":[45],"obtain":[50,94],"target":[51],"features":[52],"by":[53],"optimizing":[54],"extraction":[57],"and":[58,118,134,154,156],"enhancement,":[59],"are":[61],"not":[62],"accurate":[63],"enough":[64,101],"images.":[68],"In":[69],"this":[70,87],"paper,":[71],"we":[72],"propose":[73],"an":[74],"essential":[75,96,116,137],"mining":[77],"(EFM-net":[79],"for":[80,113,121],"short)":[81],"based":[82],"on":[83,144],"DCNN":[84],"address":[86],"issue.":[88],"Its":[89],"major":[90],"motivation":[91],"fine":[100],"distinguish":[103],"between":[104],"similar":[105],"instances.":[106],"The":[107,159],"proposed":[108,164],"pipeline":[109],"includes":[110],"Miner":[112],"purifying":[114],"Refiner":[120],"data":[122],"augmentation.":[123],"These":[124],"two":[125,145],"modules":[126],"can":[127],"work":[128],"a":[130],"mutually":[131],"reinforcing":[132],"way,":[133],"extract":[135],"targets.":[140],"We":[141,170],"evaluate":[142],"EFM-Net":[143],"public":[146],"datasets":[149],"sensing,":[152],"FGSC-23":[153],"FGSCR-42,":[155],"our":[157,173],"Aircraft-16.":[158],"results":[160],"show":[161],"that":[162],"method":[165],"consistently":[166],"outperforms":[167],"existing":[168],"alternatives.":[169],"have":[171],"released":[172],"source":[174],"code":[175],"Github":[177],"https://github.com/JACYI/EFM-Net-Pytorch.git.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}