{"id":"https://openalex.org/W4402917150","doi":"https://doi.org/10.1109/icip51287.2024.10647994","title":"Light-Weight Self-Supervised Contrastive Learning Network For Small Sample Hyperspectral Image Classification","display_name":"Light-Weight Self-Supervised Contrastive Learning Network For Small Sample Hyperspectral Image Classification","publication_year":2024,"publication_date":"2024-09-27","ids":{"openalex":"https://openalex.org/W4402917150","doi":"https://doi.org/10.1109/icip51287.2024.10647994"},"language":"en","primary_location":{"id":"doi:10.1109/icip51287.2024.10647994","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip51287.2024.10647994","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5109556279","display_name":"Gan Yang","orcid":"https://orcid.org/0009-0001-3489-4132"},"institutions":[{"id":"https://openalex.org/I20942203","display_name":"Hainan University","ror":"https://ror.org/03q648j11","country_code":"CN","type":"education","lineage":["https://openalex.org/I20942203"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gan Yang","raw_affiliation_strings":["Hainan University,Faculty of Computer Science and Technology,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hainan University,Faculty of Computer Science and Technology,China","institution_ids":["https://openalex.org/I20942203"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100358051","display_name":"Zhaohui Wang","orcid":"https://orcid.org/0000-0002-8771-0819"},"institutions":[{"id":"https://openalex.org/I20942203","display_name":"Hainan University","ror":"https://ror.org/03q648j11","country_code":"CN","type":"education","lineage":["https://openalex.org/I20942203"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaohui Wang","raw_affiliation_strings":["Hainan University,Faculty of Computer Science and Technology,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hainan University,Faculty of Computer Science and Technology,China","institution_ids":["https://openalex.org/I20942203"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20942203"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"856","last_page":"861"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9542999863624573,"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":0.9542999863624573,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8637829422950745},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6953293681144714},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6567980051040649},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6347730755805969},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5943167209625244},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4464532434940338},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3875771462917328}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8637829422950745},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6953293681144714},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6567980051040649},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6347730755805969},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5943167209625244},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4464532434940338},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3875771462917328},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip51287.2024.10647994","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip51287.2024.10647994","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2008847349","https://openalex.org/W2465503420","https://openalex.org/W2582369608","https://openalex.org/W2775183886","https://openalex.org/W2898204262","https://openalex.org/W2901461790","https://openalex.org/W2941141441","https://openalex.org/W2955446216","https://openalex.org/W2970043552","https://openalex.org/W2982083293","https://openalex.org/W3035524453","https://openalex.org/W3044657819","https://openalex.org/W3051556114","https://openalex.org/W3095270930","https://openalex.org/W3125421184","https://openalex.org/W3126650370","https://openalex.org/W3132867842","https://openalex.org/W3133271982","https://openalex.org/W3138415272","https://openalex.org/W3191502879","https://openalex.org/W4210603071","https://openalex.org/W4226182700","https://openalex.org/W4283033668","https://openalex.org/W4317438620","https://openalex.org/W4320930577","https://openalex.org/W6774314701","https://openalex.org/W6779326418"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2070598848","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2291489469","https://openalex.org/W2546503577"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"the":[3,12,22,31,42,63,88,95,108,121],"rapid":[4],"development":[5],"of":[6,14,25,34,123],"deep":[7],"learning":[8,77,118],"has":[9,46],"greatly":[10],"improved":[11],"performance":[13,113],"hyperspectral":[15],"image":[16],"(HSI)":[17],"classification.":[18],"However,":[19],"due":[20],"to":[21,61,81,93],"high":[23],"cost":[24],"obtaining":[26],"high-quality":[27],"HSI":[28,103],"data":[29,56,65,104],"and":[30,36,67,85],"large":[32],"amount":[33],"manpower":[35],"material":[37],"resources":[38],"required":[39],"for":[40],"labeling,":[41],"small":[43,124],"sample":[44,96],"problem":[45],"always":[47],"been":[48],"a":[49,54,73],"challenge.":[50],"To":[51],"solve":[52],"it,":[53],"spatial-spectral":[55,83],"augmentation":[57],"method":[58],"is":[59,79],"designed":[60],"expand":[62],"limited":[64],"set":[66],"generate":[68],"new":[69],"training":[70],"samples.":[71],"Simultaneously,":[72],"light-weight":[74],"self-supervised":[75],"contrastive":[76,117],"network":[78],"conducted":[80],"extract":[82],"feature":[84],"focus":[86],"on":[87,100],"relative":[89],"relationship":[90],"between":[91],"samples":[92],"improve":[94],"discrimination.":[97],"Experimental":[98],"results":[99],"2":[101],"public":[102],"sets":[105],"demonstrate":[106],"that":[107],"proposal":[109],"could":[110],"achieve":[111],"better":[112],"than":[114],"some":[115],"state-of-the-art":[116],"methods":[119],"facing":[120],"challenge":[122],"sample.":[125]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}