{"id":"https://openalex.org/W4285273718","doi":"https://doi.org/10.1109/lgrs.2022.3178824","title":"A Band Selection Method With Masked Convolutional Autoencoder for Hyperspectral Image","display_name":"A Band Selection Method With Masked Convolutional Autoencoder for Hyperspectral Image","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4285273718","doi":"https://doi.org/10.1109/lgrs.2022.3178824"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2022.3178824","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3178824","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","Electrical Engineering Department, Zhejiang University, Zhejiang 310027, 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"]},{"raw_affiliation_string":"Electrical Engineering Department, Zhejiang University, Zhejiang 310027, 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","Electrical Engineering Department, Zhejiang University, Zhejiang 310027, 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"]},{"raw_affiliation_string":"Electrical Engineering Department, Zhejiang University, Zhejiang 310027, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024236757","display_name":"Ziqiang Hua","orcid":"https://orcid.org/0000-0001-6955-7331"},"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":"Ziqiang Hua","raw_affiliation_strings":["Department of Electrical Engineering, Zhejiang University, Hangzhou, China","Electrical Engineering Department, Zhejiang University, Zhejiang 310027, China"],"raw_orcid":"https://orcid.org/0000-0001-6955-7331","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"Electrical Engineering Department, Zhejiang University, Zhejiang 310027, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017674477","display_name":"Chaoqun Xia","orcid":"https://orcid.org/0000-0002-0167-0423"},"institutions":[{"id":"https://openalex.org/I146620803","display_name":"Wenzhou University","ror":"https://ror.org/020hxh324","country_code":"CN","type":"education","lineage":["https://openalex.org/I146620803"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaoqun Xia","raw_affiliation_strings":["College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-0167-0423","affiliations":[{"raw_affiliation_string":"College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China","institution_ids":["https://openalex.org/I146620803"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054794945","display_name":"Liaoying Zhao","orcid":"https://orcid.org/0000-0002-9276-8679"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liaoying Zhao","raw_affiliation_strings":["Department of Computer Science, Hangzhou Dianzi University, Hangzhou, China","Department of Computer Science, Hangzhou Dianzi University, Zhejiang 310027, China"],"raw_orcid":"https://orcid.org/0000-0002-9276-8679","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]},{"raw_affiliation_string":"Department of Computer Science, Hangzhou Dianzi University, Zhejiang 310027, China","institution_ids":["https://openalex.org/I50760025"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6849,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.85028249,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"19","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":0.9990000128746033,"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":0.9990000128746033,"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":0.9415000081062317,"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/T10057","display_name":"Face and Expression Recognition","score":0.9089999794960022,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8507848978042603},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.7376241087913513},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6192410588264465},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5768348574638367},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5697598457336426},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5524661540985107},{"id":"https://openalex.org/keywords/representativeness-heuristic","display_name":"Representativeness heuristic","score":0.5159995555877686},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.4292127788066864},{"id":"https://openalex.org/keywords/spectral-bands","display_name":"Spectral bands","score":0.4152677059173584},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3586427867412567},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2548004388809204},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.23219195008277893},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.14600390195846558},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1375763714313507}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8507848978042603},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.7376241087913513},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6192410588264465},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5768348574638367},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5697598457336426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5524661540985107},{"id":"https://openalex.org/C37381756","wikidata":"https://www.wikidata.org/wiki/Q20203288","display_name":"Representativeness heuristic","level":2,"score":0.5159995555877686},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.4292127788066864},{"id":"https://openalex.org/C114700698","wikidata":"https://www.wikidata.org/wiki/Q2882278","display_name":"Spectral bands","level":2,"score":0.4152677059173584},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3586427867412567},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2548004388809204},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.23219195008277893},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.14600390195846558},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1375763714313507},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2022.3178824","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3178824","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":14,"referenced_works":["https://openalex.org/W1967275758","https://openalex.org/W2138038253","https://openalex.org/W2166923144","https://openalex.org/W2316226477","https://openalex.org/W2589377231","https://openalex.org/W2776449634","https://openalex.org/W2789249105","https://openalex.org/W2937638900","https://openalex.org/W2956920381","https://openalex.org/W2981951045","https://openalex.org/W3105005050","https://openalex.org/W3120808882","https://openalex.org/W3170936119","https://openalex.org/W3199452926"],"related_works":["https://openalex.org/W1978077614","https://openalex.org/W1982418987","https://openalex.org/W2093135244","https://openalex.org/W2137205624","https://openalex.org/W2799746630","https://openalex.org/W2040117879","https://openalex.org/W2889956472","https://openalex.org/W2924463920","https://openalex.org/W4327563507","https://openalex.org/W4310079726"],"abstract_inverted_index":{"Band":[0],"selection":[1,145],"(BS)":[2],"is":[3,82,171],"an":[4],"effective":[5],"means":[6],"to":[7,26,147,173],"solve":[8],"the":[9,30,37,48,92,97,101,105,110,122,126,137,150,158,168],"problems":[10],"of":[11,36,43,47,104,113],"spectral":[12],"redundancy":[13,108],"and":[14,33,41,72,99,109,119,125,141,170],"Hughes":[15],"phenomenon":[16],"in":[17,76],"hyperspectral":[18],"images":[19],"(HSIs).":[20],"However,":[21],"existing":[22],"BS":[23,63],"methods":[24],"fail":[25],"take":[27],"into":[28],"account":[29],"representativeness,":[31,70],"redundancy,":[32,71],"information":[34,73,103,111,127],"content":[35,74,112],"selected":[38],"bands":[39,98],"simultaneously,":[40],"most":[42],"them":[44],"lack":[45],"consideration":[46],"inherent":[49,93],"nonlinear":[50,94],"relationship":[51,95],"between":[52,96],"bands.":[53,175],"To":[54],"address":[55],"these":[56,133],"problems,":[57],"we":[58],"propose":[59],"a":[60,85,114],"novel":[61],"unsupervised":[62],"framework":[64],"that":[65,157],"can":[66,90,162],"comprehensively":[67],"consider":[68],"band":[69,80,115,152],"(RRI)":[75],"this":[77],"letter.":[78],"The":[79,107],"representativeness":[81],"estimated":[83],"by":[84,121],"three-dimensional":[86],"convolutional":[87],"autoencoder,":[88],"which":[89],"capture":[91],"leverage":[100],"spatial":[102],"HSI.":[106],"subset":[116,138],"are":[117],"restricted":[118],"enhanced":[120],"correlation":[123],"coefficient":[124],"divergence,":[128],"respectively.":[129],"Subsequently,":[130],"RRI":[131,160],"combines":[132],"three":[134],"indicators":[135],"as":[136],"evaluation":[139],"criterion":[140],"utilizes":[142],"immune":[143],"clone":[144],"algorithm":[146],"search":[148],"for":[149],"desired":[151],"subset.":[153],"Experimental":[154],"results":[155],"verify":[156],"proposed":[159],"method":[161],"provide":[163],"higher":[164],"classification":[165],"accuracy":[166],"than":[167],"competitors":[169],"robust":[172],"noisy":[174]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
