{"id":"https://openalex.org/W4308235813","doi":"https://doi.org/10.1109/icip46576.2022.9897901","title":"Active Learning for Hyperspectral Image Classification via Hypergraph Neural Network","display_name":"Active Learning for Hyperspectral Image Classification via Hypergraph Neural Network","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4308235813","doi":"https://doi.org/10.1109/icip46576.2022.9897901"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897901","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897901","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-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/A5100355570","display_name":"Yongqing Sun","orcid":"https://orcid.org/0000-0003-3116-2371"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yongqing Sun","raw_affiliation_strings":["Computer and Data Science Laboratories,Yokosuka,Kanagawa,Japan,239-0847"],"affiliations":[{"raw_affiliation_string":"Computer and Data Science Laboratories,Yokosuka,Kanagawa,Japan,239-0847","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059839991","display_name":"Anyong Qin","orcid":"https://orcid.org/0000-0002-2538-822X"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Anyong Qin","raw_affiliation_strings":["Chongqing University of Posts and Telecommunications,School of Communication and Information Engineering,Chongqing,China,400065","Chongqing Key Laboratory of Signal and Information Processing, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"Chongqing University of Posts and Telecommunications,School of Communication and Information Engineering,Chongqing,China,400065","institution_ids":["https://openalex.org/I10535382"]},{"raw_affiliation_string":"Chongqing Key Laboratory of Signal and Information Processing, Chongqing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037810535","display_name":"Yukihiro Bandoh","orcid":"https://orcid.org/0000-0001-5877-324X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yukihiro Bandoh","raw_affiliation_strings":["Computer and Data Science Laboratories,Yokosuka,Kanagawa,Japan,239-0847"],"affiliations":[{"raw_affiliation_string":"Computer and Data Science Laboratories,Yokosuka,Kanagawa,Japan,239-0847","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021881939","display_name":"Chenqiang Gao","orcid":"https://orcid.org/0000-0003-4174-4148"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenqiang Gao","raw_affiliation_strings":["Chongqing University of Posts and Telecommunications,School of Communication and Information Engineering,Chongqing,China,400065","Chongqing Key Laboratory of Signal and Information Processing, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"Chongqing University of Posts and Telecommunications,School of Communication and Information Engineering,Chongqing,China,400065","institution_ids":["https://openalex.org/I10535382"]},{"raw_affiliation_string":"Chongqing Key Laboratory of Signal and Information Processing, Chongqing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083236482","display_name":"Yusuke Hiwasaki","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yusuke Hiwasaki","raw_affiliation_strings":["Computer and Data Science Laboratories,Yokosuka,Kanagawa,Japan,239-0847"],"affiliations":[{"raw_affiliation_string":"Computer and Data Science Laboratories,Yokosuka,Kanagawa,Japan,239-0847","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100355570"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9591,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.77648157,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2576","last_page":"2580"},"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9793999791145325,"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"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.974399983882904,"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/hypergraph","display_name":"Hypergraph","score":0.9231548309326172},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7654998898506165},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.7651809453964233},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7413859367370605},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6515417098999023},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6401889324188232},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6115650534629822},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.585587203502655},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5782097578048706},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5225214958190918},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2814318537712097},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.10832694172859192}],"concepts":[{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.9231548309326172},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7654998898506165},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7651809453964233},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7413859367370605},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6515417098999023},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6401889324188232},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6115650534629822},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.585587203502655},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5782097578048706},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5225214958190918},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2814318537712097},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.10832694172859192},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897901","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897901","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W569478347","https://openalex.org/W1588948804","https://openalex.org/W2062432961","https://openalex.org/W2113464037","https://openalex.org/W2124244761","https://openalex.org/W2159284541","https://openalex.org/W2518897583","https://openalex.org/W2600061660","https://openalex.org/W2610034660","https://openalex.org/W2611452721","https://openalex.org/W2614256707","https://openalex.org/W2762884213","https://openalex.org/W2808098982","https://openalex.org/W2809113079","https://openalex.org/W2892621946","https://openalex.org/W2962702700","https://openalex.org/W2991494819","https://openalex.org/W2996636458","https://openalex.org/W3002244172","https://openalex.org/W3004925702","https://openalex.org/W3028306149","https://openalex.org/W3047443805","https://openalex.org/W3085990079","https://openalex.org/W3103695279","https://openalex.org/W3103753223","https://openalex.org/W3104795559","https://openalex.org/W3105255022","https://openalex.org/W3105357426","https://openalex.org/W3185173125","https://openalex.org/W4225630686","https://openalex.org/W6726873649"],"related_works":["https://openalex.org/W4376608589","https://openalex.org/W2072166414","https://openalex.org/W3138003926","https://openalex.org/W4300037846","https://openalex.org/W1630514295","https://openalex.org/W1537073411","https://openalex.org/W3209970181","https://openalex.org/W2963081352","https://openalex.org/W2070598848","https://openalex.org/W2084942241"],"abstract_inverted_index":{"Graph":[0],"convolution":[1],"network":[2,94,162],"(GCN)":[3],"has":[4],"been":[5],"extensively":[6],"applied":[7],"to":[8,42],"the":[9,17,23,30,34,44,50,62,66,78,84,97,104,109,111,124,132,137,147,157,169],"area":[10],"of":[11,36,108,154],"hyperspectral":[12,85],"image":[13],"(HSI)":[14],"classification.":[15],"However,":[16],"graph":[18],"can":[19,76,164],"not":[20],"effectively":[21],"describe":[22],"complex":[24,79],"relationships":[25,81],"between":[26],"HSI":[27,53,114,155,171],"pixels":[28,115],"and":[29,65,150],"GCN":[31,51],"still":[32],"faces":[33],"challenge":[35],"insufficient":[37],"labeled":[38,99,134],"pixels.":[39,135],"In":[40],"order":[41],"alleviate":[43],"above":[45],"two":[46],"issues":[47],"faced":[48],"by":[49],"in":[52,83],"classification,":[54,156],"we":[55,71,88,122],"propose":[56],"a":[57,73,90],"novel":[58],"framework":[59],"that":[60,75],"integrates":[61],"active":[63,151,159],"learning":[64,152],"hypergraph":[67,74,92,160],"neural":[68,93,161],"network.":[69],"First,":[70],"construct":[72],"reveal":[77],"non-pairwise":[80],"embedded":[82],"images.":[86],"Next,":[87],"train":[89],"semi-supervised":[91],"(GNN)":[95],"with":[96,126,131,146],"fewer":[98],"training":[100,128],"set.":[101],"Then,":[102],"exploiting":[103],"local":[105],"structural":[106],"properties":[107],"hypergraph,":[110],"most":[112],"useful":[113],"are":[116,141],"actively":[117],"selected":[118],"for":[119],"labeling.":[120],"Finally,":[121],"fine-tune":[123],"GNN":[125],"original":[127],"set":[129],"along":[130],"newly":[133],"And":[136],"last":[138],"three":[139,170],"steps":[140],"iteratively":[142],"carried":[143],"on.":[144],"Compared":[145],"other":[148],"traditional":[149],"approaches":[153],"proposed":[158],"(ACGNN)":[163],"achieve":[165],"better":[166],"performance":[167],"on":[168],"datasets.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
