{"id":"https://openalex.org/W4285277720","doi":"https://doi.org/10.1109/tgrs.2022.3179891","title":"GAN-Based Synthetic Data Augmentation for Infrared Small Target Detection","display_name":"GAN-Based Synthetic Data Augmentation for Infrared Small Target Detection","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4285277720","doi":"https://doi.org/10.1109/tgrs.2022.3179891"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3179891","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3179891","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/A5039844226","display_name":"Jun\u2010Hyung Kim","orcid":"https://orcid.org/0000-0003-2231-5209"},"institutions":[{"id":"https://openalex.org/I163753206","display_name":"Chungbuk National University","ror":"https://ror.org/02wnxgj78","country_code":"KR","type":"education","lineage":["https://openalex.org/I163753206"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jun-Hyung Kim","raw_affiliation_strings":["Department of Intelligent Systems and Robotics, Chungbuk National University, Cheongju, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0003-2231-5209","affiliations":[{"raw_affiliation_string":"Department of Intelligent Systems and Robotics, Chungbuk National University, Cheongju, Republic of Korea","institution_ids":["https://openalex.org/I163753206"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060901163","display_name":"Youngbae Hwang","orcid":"https://orcid.org/0000-0002-3400-0493"},"institutions":[{"id":"https://openalex.org/I163753206","display_name":"Chungbuk National University","ror":"https://ror.org/02wnxgj78","country_code":"KR","type":"education","lineage":["https://openalex.org/I163753206"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Youngbae Hwang","raw_affiliation_strings":["Department of Intelligent Systems and Robotics, Chungbuk National University, Cheongju, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0002-3400-0493","affiliations":[{"raw_affiliation_string":"Department of Intelligent Systems and Robotics, Chungbuk National University, Cheongju, Republic of Korea","institution_ids":["https://openalex.org/I163753206"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I163753206"],"apc_list":null,"apc_paid":null,"fwci":43.481,"has_fulltext":false,"cited_by_count":82,"citation_normalized_percentile":{"value":0.99803394,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T11856","display_name":"Thermography and Photoacoustic Techniques","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T12994","display_name":"Infrared Thermography in Medicine","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7991366386413574},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6781960129737854},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5836685299873352},{"id":"https://openalex.org/keywords/infrared","display_name":"Infrared","score":0.5748938322067261},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5294708013534546},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5241294503211975},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.49353349208831787},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44665002822875977},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4156586527824402},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4139631390571594},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.36644381284713745},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.07233545184135437}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7991366386413574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6781960129737854},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5836685299873352},{"id":"https://openalex.org/C158355884","wikidata":"https://www.wikidata.org/wiki/Q11388","display_name":"Infrared","level":2,"score":0.5748938322067261},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5294708013534546},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5241294503211975},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.49353349208831787},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44665002822875977},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4156586527824402},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4139631390571594},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.36644381284713745},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.07233545184135437},{"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/tgrs.2022.3179891","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3179891","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":[],"awards":[{"id":"https://openalex.org/G2562327577","display_name":null,"funder_award_id":"2020-0-01077","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G3841521628","display_name":null,"funder_award_id":"2020R1F1A1077110","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G4643994530","display_name":null,"funder_award_id":"2021-0-02068","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1901129140","https://openalex.org/W1910108985","https://openalex.org/W1963969078","https://openalex.org/W1974872009","https://openalex.org/W1978993121","https://openalex.org/W1983680350","https://openalex.org/W2041560658","https://openalex.org/W2051185466","https://openalex.org/W2080197848","https://openalex.org/W2125389028","https://openalex.org/W2341998679","https://openalex.org/W2415234561","https://openalex.org/W2565639579","https://openalex.org/W2578504242","https://openalex.org/W2593414223","https://openalex.org/W2603777577","https://openalex.org/W2604768956","https://openalex.org/W2805902878","https://openalex.org/W2884436604","https://openalex.org/W2884585870","https://openalex.org/W2943322963","https://openalex.org/W2962793481","https://openalex.org/W2963073614","https://openalex.org/W2963800363","https://openalex.org/W3010079414","https://openalex.org/W3014055653","https://openalex.org/W3025070692","https://openalex.org/W3032230032","https://openalex.org/W3034092487","https://openalex.org/W3034305572","https://openalex.org/W3040725977","https://openalex.org/W3046421354","https://openalex.org/W3048644861","https://openalex.org/W3082142378","https://openalex.org/W3087883562","https://openalex.org/W3115729019","https://openalex.org/W3118249006","https://openalex.org/W3118934234","https://openalex.org/W3127526425","https://openalex.org/W3130456161","https://openalex.org/W3131633592","https://openalex.org/W3131937156","https://openalex.org/W3159286275","https://openalex.org/W3164118028","https://openalex.org/W3171950886","https://openalex.org/W3193306599","https://openalex.org/W3196616669","https://openalex.org/W4212863773","https://openalex.org/W4388375774","https://openalex.org/W6678815747","https://openalex.org/W6696085341","https://openalex.org/W6744056753","https://openalex.org/W6758276651","https://openalex.org/W6759523125","https://openalex.org/W6789769854","https://openalex.org/W6797455642"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W2964954556","https://openalex.org/W3103566983","https://openalex.org/W2949096641","https://openalex.org/W2969228573","https://openalex.org/W2970686063","https://openalex.org/W4320729701","https://openalex.org/W2963690996"],"abstract_inverted_index":{"Recently,":[0],"convolution":[1],"neural":[2],"networks":[3,132,144],"(CNNs)":[4],"have":[5],"achieved":[6],"state-of-the-art":[7],"performance":[8,24,147],"in":[9,68],"infrared":[10,47,63,78,85],"small":[11,48,64],"target":[12,49,91,107],"detection.":[13,50],"However,":[14],"the":[15,23,31,53,73,88,96,100,121,134],"limited":[16],"number":[17],"of":[18,26,33,137],"public":[19,123],"training":[20,34,44],"data":[21,45,151],"restricts":[22],"improvement":[25],"CNN-based":[27],"methods.":[28],"To":[29],"handle":[30],"scarcity":[32],"data,":[35],"we":[36,76,128],"propose":[37],"a":[38],"method":[39],"that":[40,109,126],"can":[41,110],"generate":[42],"synthetic":[43,59,141],"for":[46],"We":[51],"adopt":[52],"generative":[54],"adversarial":[55],"network":[56,104],"framework":[57],"where":[58],"background":[60],"images":[61,79,83],"and":[62,140],"targets":[65],"are":[66,93],"generated":[67,113],"two":[69],"independent":[70],"processes.":[71],"In":[72,87],"first":[74],"stage,":[75,90],"synthesize":[77],"by":[80],"transforming":[81],"visible":[82],"to":[84],"ones.":[86],"second":[89],"masks":[92],"implanted":[94],"on":[95,120],"transformed":[97],"images.":[98],"Then,":[99],"proposed":[101],"intensity":[102],"modulation":[103],"synthesizes":[105],"realistic":[106],"objects":[108],"be":[111],"diversely":[112],"from":[114],"further":[115],"image":[116],"processing.":[117],"Experimental":[118],"results":[119],"recent":[122],"dataset":[124,135],"show":[125],"when":[127],"train":[129],"various":[130],"detection":[131,143],"using":[133,149],"composed":[136],"both":[138],"real":[139,150],"images,":[142],"yield":[145],"better":[146],"than":[148],"only.":[152]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":29},{"year":2024,"cited_by_count":28},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":6}],"updated_date":"2026-08-04T08:18:43.703281","created_date":"2025-10-10T00:00:00"}