{"id":"https://openalex.org/W4367031974","doi":"https://doi.org/10.1109/tgrs.2023.3270324","title":"Self-Supervised Spectral-Level Contrastive Learning for Hyperspectral Target Detection","display_name":"Self-Supervised Spectral-Level Contrastive Learning for Hyperspectral Target Detection","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4367031974","doi":"https://doi.org/10.1109/tgrs.2023.3270324"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2023.3270324","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3270324","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/A5100374604","display_name":"Yulei Wang","orcid":"https://orcid.org/0000-0001-6436-5883"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yulei Wang","raw_affiliation_strings":["Center of Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, Dalian, China"],"raw_orcid":"https://orcid.org/0000-0001-6436-5883","affiliations":[{"raw_affiliation_string":"Center of Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, Dalian, China","institution_ids":["https://openalex.org/I43313876"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000151864","display_name":"Xi Chen","orcid":"https://orcid.org/0000-0002-0016-1168"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi Chen","raw_affiliation_strings":["Center of Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, Dalian, China"],"raw_orcid":"https://orcid.org/0000-0002-0016-1168","affiliations":[{"raw_affiliation_string":"Center of Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, Dalian, China","institution_ids":["https://openalex.org/I43313876"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031035100","display_name":"Enyu Zhao","orcid":"https://orcid.org/0000-0001-7165-1861"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Enyu Zhao","raw_affiliation_strings":["Center of Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, Dalian, China"],"raw_orcid":"https://orcid.org/0000-0001-7165-1861","affiliations":[{"raw_affiliation_string":"Center of Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, Dalian, China","institution_ids":["https://openalex.org/I43313876"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101405735","display_name":"Meiping Song","orcid":"https://orcid.org/0000-0002-4489-5470"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meiping Song","raw_affiliation_strings":["Center of Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, Dalian, China"],"raw_orcid":"https://orcid.org/0000-0002-4489-5470","affiliations":[{"raw_affiliation_string":"Center of Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, Dalian, China","institution_ids":["https://openalex.org/I43313876"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I43313876"],"apc_list":null,"apc_paid":null,"fwci":6.0465,"has_fulltext":false,"cited_by_count":56,"citation_normalized_percentile":{"value":0.9707954,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"61","issue":null,"first_page":"1","last_page":"15"},"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9861000180244446,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9764000177383423,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8651367425918579},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7668467164039612},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7099753618240356},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.643975019454956},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6080543994903564},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.43967121839523315},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43295687437057495},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4202280044555664},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.20167836546897888},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.12922847270965576}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8651367425918579},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7668467164039612},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7099753618240356},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.643975019454956},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6080543994903564},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.43967121839523315},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43295687437057495},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4202280044555664},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.20167836546897888},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.12922847270965576}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2023.3270324","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3270324","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":[{"score":0.6600000262260437,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G1874286693","display_name":null,"funder_award_id":"61801075","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3866441836","display_name":null,"funder_award_id":"3132023238","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G4110561124","display_name":null,"funder_award_id":"2020M670723","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G4842089875","display_name":null,"funder_award_id":"2022-MS-160","funder_id":"https://openalex.org/F4320323086","funder_display_name":"Natural Science Foundation of Liaoning Province"},{"id":"https://openalex.org/G7167985160","display_name":null,"funder_award_id":"42271355","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"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320323086","display_name":"Natural Science Foundation of Liaoning Province","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W625476304","https://openalex.org/W1899348529","https://openalex.org/W2062741922","https://openalex.org/W2132184828","https://openalex.org/W2140219630","https://openalex.org/W2163957348","https://openalex.org/W2740976805","https://openalex.org/W2772028350","https://openalex.org/W2948763898","https://openalex.org/W2963420686","https://openalex.org/W2975159970","https://openalex.org/W2991454840","https://openalex.org/W3021150190","https://openalex.org/W3028000844","https://openalex.org/W3037613841","https://openalex.org/W3045657924","https://openalex.org/W3048175892","https://openalex.org/W3087124270","https://openalex.org/W3087782035","https://openalex.org/W3087883793","https://openalex.org/W3096751897","https://openalex.org/W3097141235","https://openalex.org/W3099850646","https://openalex.org/W3114720220","https://openalex.org/W3120451664","https://openalex.org/W3127872859","https://openalex.org/W3129532103","https://openalex.org/W3157052017","https://openalex.org/W3178914557","https://openalex.org/W3189014236","https://openalex.org/W3193418296","https://openalex.org/W3197407383","https://openalex.org/W3212625467","https://openalex.org/W6798577302","https://openalex.org/W6799905469"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2070598848","https://openalex.org/W3034375524","https://openalex.org/W2060875994","https://openalex.org/W2027399350","https://openalex.org/W2044184146","https://openalex.org/W2019190440","https://openalex.org/W4309346246","https://openalex.org/W2515319207"],"abstract_inverted_index":{"Deep":[0],"learning-based":[1,73],"hyperspectral":[2,94],"target":[3],"detection":[4,226,240],"(HTD)":[5],"methods":[6],"are":[7,100,112,143,166,184,216],"limited":[8],"by":[9,186,229],"the":[10,35,55,93,108,117,126,133,138,149,155,159,163,169,178,188,195,202,212,225,238,246],"lack":[11],"of":[12,20,54,57,125,158,182,190,197],"prior":[13,61],"information.":[14,62],"Self-supervised":[15],"learning":[16],"is":[17,129,151],"a":[18,43,69,79,89],"kind":[19],"unsupervised":[21],"learning,":[22],"which":[23],"mainly":[24],"mines":[25],"its":[26],"own":[27],"self-supervised":[28,70,90],"information":[29,222,232],"from":[30],"unlabeled":[31],"data.":[32],"By":[33],"training":[34],"model":[36,46,80],"with":[37,81,220],"such":[38],"constructed":[39,144],"valid":[40],"posterior":[41],"information,":[42],"valuable":[44],"representation":[45],"can":[47,51,204,250],"be":[48,98],"learned":[49,185],"and":[50,104,107,140,148,180],"get":[52],"rid":[53],"dependence":[56],"deep":[58],"models":[59],"on":[60],"To":[63],"this":[64,66],"end,":[65],"article":[67],"proposes":[68],"spectral-level":[71],"contrastive":[72,175],"HTD":[74,87],"(SCLHTD)":[75],"method":[76,249],"to":[77,97,115,153,168,223,236],"train":[78,116],"spectral":[82,170,174,206],"difference":[83],"discrimination":[84],"capability":[85],"for":[86,254],"in":[88,102,218],"manner.":[91],"First,":[92],"images":[95],"(HSIs)":[96],"detected":[99],"sampled":[101],"odd":[103],"even":[105],"bands,":[106],"obtained":[109],"band":[110],"subsets":[111],"then":[113,130],"used":[114,131,152,217],"corresponding":[118],"adversarial":[119],"convolutional":[120],"autoencoders.":[121],"Feature":[122],"extraction":[123],"part":[124],"trained":[127],"encoder":[128],"as":[132],"data":[134,146],"augmentation":[135],"function,":[136],"where":[137,177],"positive":[139,191],"negative":[141,198],"pairs":[142,192],"through":[145],"augmentation,":[147],"backbone":[150,203],"extract":[154],"representative":[156,164],"vectors":[157,165],"augmented":[160],"samples.":[161],"Second,":[162],"mapped":[167],"contrast":[171],"space":[172,221],"using":[173],"head,":[176],"similarity":[179,189,196,235],"dissimilarity":[181],"spectra":[183],"maximizing":[187],"while":[193],"minimizing":[194],"pairs,":[199],"so":[200],"that":[201,245],"discriminate":[205],"differences.":[207],"Finally,":[208],"aiming":[209],"at":[210],"suppressing":[211],"background,":[213],"edge-preserving":[214],"filters":[215],"conjunction":[219],"process":[224],"results":[227,243],"acquired":[228],"utilizing":[230],"spectrum":[231],"via":[233],"cosine":[234],"generate":[237],"final":[239],"results.":[241],"Experimental":[242],"illustrate":[244],"proposed":[247],"SCLHTD":[248],"achieve":[251],"superior":[252],"performances":[253],"HTD.":[255]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":21},{"year":2024,"cited_by_count":26},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-21T08:15:58.654021","created_date":"2025-10-10T00:00:00"}
