{"id":"https://openalex.org/W4312498943","doi":"https://doi.org/10.1109/igarss46834.2022.9883513","title":"Cross-Scene Hyperspectral Image Classification Based on Cycle-Consistent Adversarial Networks","display_name":"Cross-Scene Hyperspectral Image Classification Based on Cycle-Consistent Adversarial Networks","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312498943","doi":"https://doi.org/10.1109/igarss46834.2022.9883513"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9883513","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9883513","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","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/A5066160415","display_name":"Zhihao Meng","orcid":null},"institutions":[{"id":"https://openalex.org/I55538621","display_name":"China Jiliang University","ror":"https://ror.org/05v1y0t93","country_code":"CN","type":"education","lineage":["https://openalex.org/I55538621"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhihao Meng","raw_affiliation_strings":["College of Information Engineering, China Jiliang University,Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province,China","Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, China"],"affiliations":[{"raw_affiliation_string":"College of Information Engineering, China Jiliang University,Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province,China","institution_ids":["https://openalex.org/I55538621"]},{"raw_affiliation_string":"Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, China","institution_ids":["https://openalex.org/I55538621"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061001516","display_name":"Minchao Ye","orcid":"https://orcid.org/0000-0003-3608-7913"},"institutions":[{"id":"https://openalex.org/I55538621","display_name":"China Jiliang University","ror":"https://ror.org/05v1y0t93","country_code":"CN","type":"education","lineage":["https://openalex.org/I55538621"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minchao Ye","raw_affiliation_strings":["College of Information Engineering, China Jiliang University,Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province,China","Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, China"],"affiliations":[{"raw_affiliation_string":"College of Information Engineering, China Jiliang University,Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province,China","institution_ids":["https://openalex.org/I55538621"]},{"raw_affiliation_string":"Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, China","institution_ids":["https://openalex.org/I55538621"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053300322","display_name":"Futian Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I55538621","display_name":"China Jiliang University","ror":"https://ror.org/05v1y0t93","country_code":"CN","type":"education","lineage":["https://openalex.org/I55538621"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Futian Yao","raw_affiliation_strings":["College of Information Engineering, China Jiliang University,Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province,China","Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, China"],"affiliations":[{"raw_affiliation_string":"College of Information Engineering, China Jiliang University,Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province,China","institution_ids":["https://openalex.org/I55538621"]},{"raw_affiliation_string":"Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, China","institution_ids":["https://openalex.org/I55538621"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033291716","display_name":"Fengchao Xiong","orcid":"https://orcid.org/0000-0002-9753-4919"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fengchao Xiong","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology,China","School of Computer Science and Engineering, Nanjing University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology,China","institution_ids":["https://openalex.org/I36399199"]},{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059857918","display_name":"Yuntao Qian","orcid":"https://orcid.org/0000-0002-7418-5891"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuntao Qian","raw_affiliation_strings":["College of Computer Science, Zhejiang University,China","College of Computer Science, Zhejiang University, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Zhejiang University,China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"College of Computer Science, Zhejiang University, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5066160415"],"corresponding_institution_ids":["https://openalex.org/I55538621"],"apc_list":null,"apc_paid":null,"fwci":2.8759,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.94447037,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1912","last_page":"1915"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9945999979972839,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.986299991607666,"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/discriminator","display_name":"Discriminator","score":0.7859275341033936},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7716064453125},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7526315450668335},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7266014218330383},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5644192695617676},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.4823979139328003},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.4671000838279724},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4558907151222229},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.44693997502326965},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4291980564594269},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4145916700363159},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3514695167541504},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1286107301712036},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.07825925946235657}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.7859275341033936},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7716064453125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7526315450668335},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7266014218330383},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5644192695617676},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.4823979139328003},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.4671000838279724},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4558907151222229},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.44693997502326965},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4291980564594269},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4145916700363159},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3514695167541504},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1286107301712036},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.07825925946235657},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss46834.2022.9883513","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9883513","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6800000071525574}],"awards":[{"id":"https://openalex.org/G4683204612","display_name":null,"funder_award_id":"62071421,62002169","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2914331134","https://openalex.org/W2945225401","https://openalex.org/W2950776302","https://openalex.org/W2962793481","https://openalex.org/W2977002487","https://openalex.org/W3034757175","https://openalex.org/W3046476394","https://openalex.org/W3064432655","https://openalex.org/W3066454894","https://openalex.org/W3207368019","https://openalex.org/W6729482032"],"related_works":["https://openalex.org/W2995777218","https://openalex.org/W3217069185","https://openalex.org/W2784931967","https://openalex.org/W4293400715","https://openalex.org/W3049340819","https://openalex.org/W4308928038","https://openalex.org/W4200430540","https://openalex.org/W3141413246","https://openalex.org/W3112293331","https://openalex.org/W2808862658"],"abstract_inverted_index":{"Lack":[0],"of":[1,70,172],"labeled":[2,36,48,61],"training":[3],"samples":[4,37,49,62],"is":[5,15,38,50,87],"a":[6,16,76,100,104,111,138],"challenge":[7],"in":[8,63,133],"hyperspectral":[9],"image":[10],"(HSI)":[11],"classification.":[12],"Cross-scene":[13],"classification":[14,69],"valid":[17],"solution":[18],"to":[19],"few-shot":[20],"learning":[21,92],"problem.":[22],"In":[23,73,98,127],"cross-scene":[24,166],"classification,":[25],"two":[26,58,164],"strongly":[27],"related":[28],"HSI":[29,167],"scenes":[30,122],"are":[31,107],"considered,":[32],"one":[33,45],"with":[34,123],"sufficient":[35],"called":[39,51],"source":[40,64,94,119,156],"scene,":[41],"while":[42],"the":[43,68,124,152,170,173],"other":[44],"containing":[46],"limited":[47],"target":[52,71,96,121,158],"scene.":[53,72],"By":[54],"establishing":[55],"connections":[56],"between":[57,118,155],"scenes,":[59],"abundant":[60],"scene":[65],"can":[66,114,150],"benefit":[67],"this":[74],"paper,":[75],"novel":[77],"model":[78],"named":[79],"cycle":[80],"auxiliary":[81,144,148],"classifier":[82,141],"generative":[83],"adversarial":[84,125],"network":[85],"(Cycle-AC-GAN)":[86],"proposed":[88,174],"for":[89],"heterogeneous":[90],"transfer":[91],"across":[93],"and":[95,103,120,142,157],"scenes.":[97],"Cycle-AC-GAN,":[99,134],"source-to-target":[101],"generator":[102,106],"target-to-source":[105],"simultaneously":[108],"built.":[109],"Thus,":[110],"two-way":[112],"mapping":[113],"be":[115],"effectively":[116],"established":[117],"training.":[126],"addition,":[128],"different":[129],"from":[130],"existing":[131],"CycleGAN,":[132],"each":[135],"discriminator":[136],"contains":[137],"binary":[139],"domain":[140],"an":[143],"land-cover":[145],"classifier.":[146],"The":[147],"classifiers":[149],"align":[151],"class-conditional":[153],"distributions":[154],"HSIs.":[159],"Inspiring":[160],"experimental":[161],"results":[162],"on":[163],"real-world":[165],"datasets":[168],"demonstrate":[169],"effectiveness":[171],"approach.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}