{"id":"https://openalex.org/W4387802242","doi":"https://doi.org/10.1109/igarss52108.2023.10282628","title":"Graph-Based Multi-View Learning for Hyperspectral Remote Sensing Image Classification","display_name":"Graph-Based Multi-View Learning for Hyperspectral Remote Sensing Image Classification","publication_year":2023,"publication_date":"2023-07-16","ids":{"openalex":"https://openalex.org/W4387802242","doi":"https://doi.org/10.1109/igarss52108.2023.10282628"},"language":"en","primary_location":{"id":"doi:10.1109/igarss52108.2023.10282628","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss52108.2023.10282628","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","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/A5101719665","display_name":"Xiao Yu","orcid":"https://orcid.org/0000-0002-6240-8090"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao Yu","raw_affiliation_strings":["Beijing Institute of Tracking and Telecommunications Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Tracking and Telecommunications Technology","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100381999","display_name":"Qiang Zhang","orcid":"https://orcid.org/0000-0003-3776-9799"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiang Zhang","raw_affiliation_strings":["Beijing Institute of Tracking and Telecommunications Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Tracking and Telecommunications Technology","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":"12","issue":null,"first_page":"7222","last_page":"7225"},"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9247757792472839},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7189068794250488},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6488853693008423},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6431868076324463},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6206979751586914},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5831984877586365},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.5698089599609375},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5295560956001282},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.49343982338905334},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4548318088054657},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.43676021695137024},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35848814249038696},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.2655943036079407},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09063827991485596},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.07074269652366638}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9247757792472839},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7189068794250488},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6488853693008423},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6431868076324463},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6206979751586914},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5831984877586365},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.5698089599609375},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5295560956001282},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.49343982338905334},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4548318088054657},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.43676021695137024},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35848814249038696},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2655943036079407},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09063827991485596},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.07074269652366638},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss52108.2023.10282628","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss52108.2023.10282628","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2069231830","https://openalex.org/W2345059010","https://openalex.org/W2921065608","https://openalex.org/W2940678725","https://openalex.org/W3125460671","https://openalex.org/W3127593446","https://openalex.org/W3129109516","https://openalex.org/W4226275810","https://openalex.org/W4229042884"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W4312814274","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W2317401237","https://openalex.org/W1990800631","https://openalex.org/W2167120702","https://openalex.org/W2579567122"],"abstract_inverted_index":{"Hyperspectral":[0],"imaging":[1],"has":[2],"been":[3],"widely":[4],"used":[5],"in":[6,51],"the":[7,44,78,108,121,125,140,144,148,154,158,161,181],"remote":[8],"sensing":[9],"due":[10],"to":[11,27,76,87,138],"its":[12],"ability":[13],"of":[14,43,71,81,153,160,183],"capturing":[15],"increasingly":[16],"rich":[17],"spectral":[18,45,83,102,112,128],"information.":[19],"However,":[20],"it":[21],"is":[22,75,136],"still":[23],"a":[24,131],"hard":[25],"work":[26],"get":[28],"satisfied":[29],"interpretation":[30],"accuracies":[31],"when":[32],"dealing":[33],"with":[34,124,173],"such":[35],"increased":[36],"spectral/spatial":[37],"resolution":[38],"images.":[39],"To":[40,156],"take":[41],"advantages":[42],"signatures":[46],"and":[47,84,101,107,111,127,171],"spatial":[48,85,99,109,126],"information":[49,86],"contained":[50],"hyperspectral":[52],"image":[53],"(HSI),":[54],"this":[55],"paper":[56],"proposes":[57],"an":[58],"efficient":[59],"graph":[60],"based":[61],"multi-view":[62],"clustering":[63,134],"for":[64],"unsupervised":[65],"HSI":[66],"classification.":[67],"The":[68,177],"key":[69],"idea":[70],"our":[72,184],"proposed":[73,162,185],"algorithm":[74,135],"exploit":[77],"mutual":[79],"agreement":[80],"both":[82],"obtain":[88],"better":[89],"classification":[90],"performance":[91,159],"than":[92],"using":[93],"any":[94],"single":[95],"view":[96,129],"data.":[97],"Firstly,":[98],"features":[100,103,110,113],"are":[104,114],"extracted":[105],"separately":[106],"considered":[115],"as":[116],"two":[117,145,168],"different":[118],"views":[119,146],"about":[120],"HSI.":[122,155],"Secondly,":[123],"representations,":[130],"graph-based":[132],"multiview":[133],"designed":[137],"generate":[139],"cluster":[141,151],"labels":[142],"since":[143],"admit":[147],"same":[149],"underlying":[150],"structure":[152],"evaluate":[157],"method,":[163],"we":[164],"conduct":[165],"experiments":[166],"on":[167],"benchmark":[169],"datasets":[170],"compare":[172],"six":[174],"state-of-the-art":[175],"approaches.":[176],"experimental":[178],"results":[179],"confirm":[180],"effectiveness":[182],"method.":[186]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}