{"id":"https://openalex.org/W4396628263","doi":"https://doi.org/10.1145/3647649.3647699","title":"Feature Mixture Generative Adversarial Network for Data Augmentation on Small Sample Hyperspectral Data","display_name":"Feature Mixture Generative Adversarial Network for Data Augmentation on Small Sample Hyperspectral Data","publication_year":2024,"publication_date":"2024-01-19","ids":{"openalex":"https://openalex.org/W4396628263","doi":"https://doi.org/10.1145/3647649.3647699"},"language":"en","primary_location":{"id":"doi:10.1145/3647649.3647699","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3647649.3647699","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 7th International Conference on Image and Graphics Processing","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/A5102961613","display_name":"Yulin Li","orcid":"https://orcid.org/0009-0006-4926-9838"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yulin Li","raw_affiliation_strings":["College of Information Science and Technology, Beijing University of Chemical Technology, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing University of Chemical Technology, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100442192","display_name":"Mengmeng Zhang","orcid":"https://orcid.org/0000-0002-5724-9785"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengmeng Zhang","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114948798","display_name":"Xiaoming Xie","orcid":"https://orcid.org/0009-0002-5992-4630"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoming Xie","raw_affiliation_strings":["College of Information Science and Technology, Beijing University of Chemical Technology, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing University of Chemical Technology, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033234889","display_name":"Yunhao Gao","orcid":"https://orcid.org/0000-0002-2896-6902"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunhao Gao","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100317994","display_name":"Wei Li","orcid":"https://orcid.org/0000-0001-7015-7335"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5102961613"],"corresponding_institution_ids":["https://openalex.org/I75390827"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11164313,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"316","last_page":"321"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9986000061035156,"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":0.9986000061035156,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9972000122070312,"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/T10057","display_name":"Face and Expression Recognition","score":0.9939000010490417,"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/discriminator","display_name":"Discriminator","score":0.8134843707084656},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7389701008796692},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6274092197418213},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5557665228843689},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.506381630897522},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4974983036518097},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.4909384548664093},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4857504963874817},{"id":"https://openalex.org/keywords/data-redundancy","display_name":"Data redundancy","score":0.450623095035553},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42914992570877075},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41594335436820984},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32604843378067017},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.23917821049690247}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.8134843707084656},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7389701008796692},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6274092197418213},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5557665228843689},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.506381630897522},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4974983036518097},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.4909384548664093},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4857504963874817},{"id":"https://openalex.org/C7545210","wikidata":"https://www.wikidata.org/wiki/Q838123","display_name":"Data redundancy","level":2,"score":0.450623095035553},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42914992570877075},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41594335436820984},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32604843378067017},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.23917821049690247},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","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/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"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:10.1145/3647649.3647699","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3647649.3647699","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 7th International Conference on Image and Graphics Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2618017917","https://openalex.org/W2762818368","https://openalex.org/W2785678896","https://openalex.org/W2886848336","https://openalex.org/W2903091095","https://openalex.org/W2963158472","https://openalex.org/W2970170226","https://openalex.org/W2973375236","https://openalex.org/W3010188879","https://openalex.org/W3034916338","https://openalex.org/W3092424322","https://openalex.org/W3129153764","https://openalex.org/W3129747129","https://openalex.org/W3153990350","https://openalex.org/W3155739706","https://openalex.org/W3172300865","https://openalex.org/W3201706071","https://openalex.org/W4200635287","https://openalex.org/W4230256404","https://openalex.org/W4285814588","https://openalex.org/W4312355426","https://openalex.org/W4362588283","https://openalex.org/W4379279743","https://openalex.org/W4394669348"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W3200695403","https://openalex.org/W4288087796","https://openalex.org/W4321441197","https://openalex.org/W2982339091"],"abstract_inverted_index":{"With":[0],"the":[1,24,27,79,82,89,95,104,110,115,133,137,143,150,156,159],"development":[2],"of":[3,26,30,81,91,121,145,158],"remote":[4,7,39,65,126],"sensing":[5,8,40,66,127],"technology,":[6],"data":[9,31,41,112,134,146],"has":[10],"been":[11],"widely":[12],"used":[13],"in":[14,50],"agriculture,":[15],"medicine,":[16],"military,":[17],"and":[18,33,147],"other":[19],"fields.":[20],"However,":[21],"due":[22],"to":[23,63,77,102,114],"disadvantages":[25],"high":[28,34],"cost":[29],"collection":[32],"redundancy,":[35],"regression":[36],"experiments":[37,123],"using":[38],"have":[42],"serious":[43],"overfitting":[44],"problems.":[45],"It":[46],"limits":[47],"its":[48],"application":[49],"practical":[51],"work.":[52],"To":[53],"alleviate":[54],"this":[55,69],"problem,":[56],"we":[57],"propose":[58],"a":[59,71,119],"generative":[60,138],"adversarial":[61,139],"network":[62,140],"generate":[64],"signals.":[67],"In":[68],"paper,":[70],"feature":[72],"mixing":[73],"module":[74],"was":[75],"proposed":[76],"reduce":[78],"bias":[80],"discriminator":[83],"for":[84],"different":[85],"signals,":[86],"thereby":[87],"increasing":[88],"diversity":[90,144],"generated":[92,111,135],"data.":[93,160],"At":[94],"same":[96],"time,":[97],"spectral":[98],"normalization":[99],"is":[100,130],"utilized":[101],"improve":[103],"stability":[105],"during":[106],"generation,":[107],"which":[108],"makes":[109],"closer":[113],"real":[116],"signal.":[117],"After":[118],"series":[120],"ablation":[122],"on":[124,154],"small-sample":[125],"data,":[128],"it":[129],"proved":[131],"that":[132],"by":[136],"significantly":[141],"improves":[142],"effectively":[148],"alleviates":[149],"over-fitting":[151],"problem":[152],"based":[153],"ensuring":[155],"reliability":[157]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
