{"id":"https://openalex.org/W4390097175","doi":"https://doi.org/10.1109/tgrs.2023.3345923","title":"SPFormer: Self-Pooling Transformer for Few-Shot Hyperspectral Image Classification","display_name":"SPFormer: Self-Pooling Transformer for Few-Shot Hyperspectral Image Classification","publication_year":2023,"publication_date":"2023-12-22","ids":{"openalex":"https://openalex.org/W4390097175","doi":"https://doi.org/10.1109/tgrs.2023.3345923"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2023.3345923","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3345923","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/A5100753804","display_name":"Ziyu Li","orcid":"https://orcid.org/0000-0002-6520-2530"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziyu Li","raw_affiliation_strings":["School of Earth Sciences and Engineering, Hohai University, Nanjing, China","College of Geography and Remote Sensing, Hohai University, Nanjing, China","Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-6520-2530","affiliations":[{"raw_affiliation_string":"School of Earth Sciences and Engineering, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"College of Geography and Remote Sensing, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018243278","display_name":"Zhaohui Xue","orcid":"https://orcid.org/0000-0001-6253-2967"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaohui Xue","raw_affiliation_strings":["School of Earth Sciences and Engineering, Hohai University, Nanjing, China","College of Geography and Remote Sensing, Hohai University, Nanjing, China","Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-6253-2967","affiliations":[{"raw_affiliation_string":"School of Earth Sciences and Engineering, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"College of Geography and Remote Sensing, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101586159","display_name":"Qi Xu","orcid":"https://orcid.org/0000-0003-0863-1782"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi Xu","raw_affiliation_strings":["Beijing Data Intelligence Information Technology Company Ltd., Wuhan Innovation Center, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Data Intelligence Information Technology Company Ltd., Wuhan Innovation Center, Wuhan, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100450721","display_name":"Ling Zhang","orcid":"https://orcid.org/0000-0001-8206-3408"},"institutions":[{"id":"https://openalex.org/I4210153872","display_name":"Jiangsu Maritime Institute","ror":"https://ror.org/04pekda06","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Zhang","raw_affiliation_strings":["School of Naval Architecture and Ocean Engineering, Jiangsu Maritime Institute, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Naval Architecture and Ocean Engineering, Jiangsu Maritime Institute, Nanjing, China","institution_ids":["https://openalex.org/I4210153872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101960921","display_name":"Tianzhi Zhu","orcid":"https://orcid.org/0000-0002-8294-2274"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianzhi Zhu","raw_affiliation_strings":["School of Earth Sciences and Engineering, Hohai University, Nanjing, China","College of Geography and Remote Sensing, Hohai University, Nanjing, China","Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-8294-2274","affiliations":[{"raw_affiliation_string":"School of Earth Sciences and Engineering, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"College of Geography and Remote Sensing, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016879046","display_name":"Mengxue Zhang","orcid":"https://orcid.org/0000-0002-8587-4334"},"institutions":[{"id":"https://openalex.org/I16097986","display_name":"Universitat de Val\u00e8ncia","ror":"https://ror.org/043nxc105","country_code":"ES","type":"education","lineage":["https://openalex.org/I16097986"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Mengxue Zhang","raw_affiliation_strings":["Image and Signal Processing (ISP) Group, University of Valencia, Valencia, Spain"],"raw_orcid":"https://orcid.org/0000-0002-8587-4334","affiliations":[{"raw_affiliation_string":"Image and Signal Processing (ISP) Group, University of Valencia, Valencia, Spain","institution_ids":["https://openalex.org/I16097986"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.0025,"has_fulltext":false,"cited_by_count":56,"citation_normalized_percentile":{"value":0.97099708,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"62","issue":null,"first_page":"1","last_page":"19"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9988999962806702,"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":0.9988999962806702,"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":0.9646000266075134,"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"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.95660001039505,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/computer-science","display_name":"Computer science","score":0.7468812465667725},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7393981218338013},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6421618461608887},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.613006055355072},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5195258259773254},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5171270370483398},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5059835314750671},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.49789857864379883},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49021202325820923},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4758831262588501},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42192184925079346},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.321329802274704},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1573127806186676},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08001008629798889}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7468812465667725},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7393981218338013},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6421618461608887},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.613006055355072},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5195258259773254},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5171270370483398},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5059835314750671},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.49789857864379883},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49021202325820923},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4758831262588501},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42192184925079346},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.321329802274704},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1573127806186676},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08001008629798889},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2023.3345923","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3345923","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/G179363741","display_name":"\u9ad8\u5149\u8c31\u9065\u611f\u5f71\u50cf\u534a\u76d1\u7763\u6df1\u5ea6\u5b66\u4e60\u4e0e\u5206\u7c7b\u7814\u7a76","funder_award_id":"41971279","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1827510721","display_name":null,"funder_award_id":"22KJD420001","funder_id":"https://openalex.org/F4320335440","funder_display_name":"Natural Science Research of Jiangsu Higher Education Institutions of China"},{"id":"https://openalex.org/G2659737875","display_name":null,"funder_award_id":"42271324","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5116539810","display_name":null,"funder_award_id":"BK20221506","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"},{"id":"https://openalex.org/G6956892774","display_name":null,"funder_award_id":"42201406","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/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335440","display_name":"Natural Science Research of Jiangsu Higher Education Institutions of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":69,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W1950365613","https://openalex.org/W2029316659","https://openalex.org/W2069231830","https://openalex.org/W2090424610","https://openalex.org/W2136251662","https://openalex.org/W2209969974","https://openalex.org/W2261059368","https://openalex.org/W2464755555","https://openalex.org/W2500751094","https://openalex.org/W2601450892","https://openalex.org/W2602024454","https://openalex.org/W2604763608","https://openalex.org/W2764276316","https://openalex.org/W2791006446","https://openalex.org/W2800371750","https://openalex.org/W2808098982","https://openalex.org/W2898204262","https://openalex.org/W2914331134","https://openalex.org/W2936517889","https://openalex.org/W2940678725","https://openalex.org/W2952956606","https://openalex.org/W2961290969","https://openalex.org/W2962770389","https://openalex.org/W2963125010","https://openalex.org/W2964105864","https://openalex.org/W2971262044","https://openalex.org/W2989871747","https://openalex.org/W2998247074","https://openalex.org/W3012405452","https://openalex.org/W3015746332","https://openalex.org/W3037613841","https://openalex.org/W3098388691","https://openalex.org/W3105357426","https://openalex.org/W3120356488","https://openalex.org/W3128776197","https://openalex.org/W3132867842","https://openalex.org/W3133055443","https://openalex.org/W3138725786","https://openalex.org/W3146366485","https://openalex.org/W3181729304","https://openalex.org/W3185173125","https://openalex.org/W3193205520","https://openalex.org/W3193242948","https://openalex.org/W3200760345","https://openalex.org/W3202805802","https://openalex.org/W3204445362","https://openalex.org/W3205614732","https://openalex.org/W3213631637","https://openalex.org/W3214821343","https://openalex.org/W4206578920","https://openalex.org/W4210794570","https://openalex.org/W4240485910","https://openalex.org/W4281394103","https://openalex.org/W4285106080","https://openalex.org/W4285127355","https://openalex.org/W4285293944","https://openalex.org/W4295308382","https://openalex.org/W4296339430","https://openalex.org/W4312820606","https://openalex.org/W4312975848","https://openalex.org/W4318483117","https://openalex.org/W4323338598","https://openalex.org/W4327521657","https://openalex.org/W4367359538","https://openalex.org/W6735236233","https://openalex.org/W6736057607","https://openalex.org/W6739901393","https://openalex.org/W6740474667"],"related_works":["https://openalex.org/W2159052453","https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2072166414","https://openalex.org/W2752972570","https://openalex.org/W2748667022","https://openalex.org/W3211770882","https://openalex.org/W2010629964","https://openalex.org/W2983135586"],"abstract_inverted_index":{"Transformer":[0,76],"has":[1],"shown":[2],"great":[3],"potential":[4],"in":[5,60,63,171],"extracting":[6],"global":[7],"features,":[8],"and":[9,47,113,127,176],"it":[10],"can":[11],"achieve":[12],"better":[13],"classification":[14,41,156,172],"performance":[15,62,157],"with":[16,24,109,184],"a":[17,64,73,85,135,147],"large":[18],"number":[19],"of":[20,32,98,158],"training":[21,186],"samples":[22],"compared":[23,179],"other":[25],"deep":[26,143],"learning":[27,66,91],"(DL)":[28],"models.":[29],"However,":[30],"most":[31],"the":[33,44,70,96,159],"existing":[34,181],"Transformer-based":[35],"models":[36],"for":[37,80,106,120,153],"hyperspectral":[38],"image":[39],"(HSI)":[40],"simply":[42],"use":[43],"multihead":[45,107],"self-attention":[46],"channel":[48,104,137],"multilayer":[49],"perceptron":[50],"(MLP)":[51],"modules":[52],"that":[53],"contain":[54],"many":[55],"parameters":[56],"to":[57,94,124,141,180],"learn,":[58],"resulting":[59],"poor":[61],"few-shot":[65,81],"scenario.":[67],"To":[68],"overcome":[69],"above":[71],"issue,":[72],"lightweight":[74,136],"self-pooling":[75,108],"(SPFormer)":[77],"is":[78,92,139,151,162],"proposed":[79,119,160],"HSI":[82],"classification.":[83,154],"First,":[84],"one-layer":[86],"autoencoder":[87],"based":[88],"on":[89,164],"self-supervised":[90],"built":[93],"reduce":[95],"dimensionality":[97],"HSI.":[99],"Second,":[100],"two":[101],"parameter-free":[102],"modules,":[103],"shuffle":[105],"sparse":[110],"mapping":[111,121],"(CSSM-MHSP)":[112],"central":[114],"token":[115],"mixer":[116],"(CTM),":[117],"are":[118],"spectral":[122,144],"features":[123],"higher":[125],"dimensions":[126],"promoting":[128],"information":[129],"interaction":[130],"between":[131],"pixels,":[132],"respectively.":[133],"Third,":[134],"embedding":[138],"designed":[140],"extract":[142],"features.":[145],"Finally,":[146],"fully":[148],"connected":[149],"layer":[150],"used":[152],"The":[155],"method":[161],"evaluated":[163],"four":[165],"benchmark":[166],"datasets,":[167],"showing":[168],"its":[169],"superiority":[170],"accuracy,":[173],"generalization":[174],"performance,":[175],"model":[177],"complexity":[178],"state-of-the-art":[182],"methods":[183],"limited":[185],"samples.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":35},{"year":2024,"cited_by_count":16}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}