{"id":"https://openalex.org/W4386487764","doi":"https://doi.org/10.1109/lgrs.2023.3297746","title":"BSFormer: Transformer-Based Reconstruction Network for Hyperspectral Band Selection","display_name":"BSFormer: Transformer-Based Reconstruction Network for Hyperspectral Band Selection","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4386487764","doi":"https://doi.org/10.1109/lgrs.2023.3297746"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2023.3297746","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3297746","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5101660606","display_name":"Yufei Liu","orcid":"https://orcid.org/0000-0002-1164-4931"},"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":"Yufei Liu","raw_affiliation_strings":["Department of Electrical Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-1164-4931","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062771797","display_name":"Xiaorun Li","orcid":"https://orcid.org/0000-0002-4312-7533"},"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":"Xiaorun Li","raw_affiliation_strings":["Department of Electrical Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-4312-7533","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046624090","display_name":"Zezhong Xu","orcid":"https://orcid.org/0000-0003-2463-4270"},"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":"Zezhong Xu","raw_affiliation_strings":["Department of Electrical Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024236757","display_name":"Ziqiang Hua","orcid":"https://orcid.org/0000-0001-6955-7331"},"institutions":[{"id":"https://openalex.org/I168879160","display_name":"Zhejiang University of Science and Technology","ror":"https://ror.org/05mx0wr29","country_code":"CN","type":"education","lineage":["https://openalex.org/I168879160"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziqiang Hua","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-6955-7331","affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I168879160"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.714,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.85291341,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"20","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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":1.0,"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":0.9936000108718872,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9929999709129333,"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":0.7960317134857178},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7071513533592224},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.6417813301086426},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6066058874130249},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.580322802066803},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5539820194244385},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5162953734397888},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4960258901119232},{"id":"https://openalex.org/keywords/representativeness-heuristic","display_name":"Representativeness heuristic","score":0.45298534631729126},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3531767725944519},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15467679500579834},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11063188314437866},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.09390208125114441}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7960317134857178},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7071513533592224},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.6417813301086426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6066058874130249},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.580322802066803},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5539820194244385},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5162953734397888},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4960258901119232},{"id":"https://openalex.org/C37381756","wikidata":"https://www.wikidata.org/wiki/Q20203288","display_name":"Representativeness heuristic","level":2,"score":0.45298534631729126},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3531767725944519},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15467679500579834},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11063188314437866},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.09390208125114441},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"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/lgrs.2023.3297746","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3297746","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6472963248","display_name":null,"funder_award_id":"62171404","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":13,"referenced_works":["https://openalex.org/W2316226477","https://openalex.org/W2789249105","https://openalex.org/W2907064575","https://openalex.org/W2937638900","https://openalex.org/W2997272341","https://openalex.org/W3090548235","https://openalex.org/W3105005050","https://openalex.org/W3120808882","https://openalex.org/W3170936119","https://openalex.org/W3216703721","https://openalex.org/W4285189165","https://openalex.org/W4285273718","https://openalex.org/W6739901393"],"related_works":["https://openalex.org/W3159631231","https://openalex.org/W4306248409","https://openalex.org/W4211213551","https://openalex.org/W2332151799","https://openalex.org/W2062728131","https://openalex.org/W3013693939","https://openalex.org/W1824075546","https://openalex.org/W2103926897","https://openalex.org/W2101250918","https://openalex.org/W4376143407"],"abstract_inverted_index":{"Band":[0],"selection":[1],"(BS)":[2],"is":[3],"an":[4],"effective":[5],"approach":[6],"to":[7,28,31,44,105],"alleviate":[8],"the":[9,47,56,68,72,99,107,111,149,152,157,166,183,192],"spectral":[10,36],"redundancy":[11,143],"of":[12,49,55,58,75,110,114,139,151],"a":[13,24,53,88,115,122,133],"hyperspectral":[14,179],"image":[15],"(HSI).":[16],"The":[17,160],"emerging":[18],"deep-learning-based":[19,40],"BS":[20,41],"methods":[21,42,64],"have":[22],"become":[23],"hot":[25],"topic":[26],"due":[27],"their":[29],"ability":[30],"model":[32],"nonlinear":[33],"relationships":[34],"between":[35],"bands.":[37],"However,":[38],"existing":[39,62],"fail":[43],"accurately":[45],"extract":[46],"representativeness":[48,168],"each":[50],"band":[51,117,135,167],"as":[52],"result":[54],"limitation":[57],"interpretation":[59],"networks.":[60],"Moreover,":[61],"deep-learning":[63],"cannot":[65],"fully":[66,164],"utilize":[67],"interband":[69,170],"correlation":[70,171],"and":[71,121,142,169],"spatial":[73],"information":[74,109],"HSIs":[76],"for":[77,93,172],"BS.":[78,173],"To":[79],"solve":[80],"these":[81],"issues,":[82],"in":[83,156],"this":[84,129],"letter,":[85],"we":[86,131],"propose":[87],"novel":[89,134],"Transformer":[90,100],"reconstruction":[91,101,126],"network":[92],"unsupervised":[94],"BS,":[95],"termed":[96],"BSFormer.":[97],"Specifically,":[98],"network,":[102],"which":[103,145],"contributes":[104],"leveraging":[106],"spectral\u2013spatial":[108],"HSI,":[112],"consists":[113],"Transformer-based":[116],"attention":[118],"(TBA)":[119],"module":[120],"convolutional":[123],"autoencoder":[124],"(CAE)-based":[125],"module.":[127,159],"On":[128],"basis,":[130],"design":[132],"evaluation":[136],"criterion":[137,162],"composed":[138],"representative":[140],"metric":[141],"metric,":[144],"are":[146],"interpreted":[147],"with":[148],"help":[150],"multihead":[153],"self-attention":[154],"layer":[155],"TBA":[158],"designed":[161],"can":[163,186],"use":[165],"Experimental":[174],"results":[175],"on":[176],"three":[177],"well-known":[178],"datasets":[180],"verify":[181],"that":[182],"proposed":[184],"BSFormer":[185],"yield":[187],"better":[188],"classification":[189],"performance":[190],"than":[191],"competitors.":[193]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}