{"id":"https://openalex.org/W3174003606","doi":"https://doi.org/10.1109/lgrs.2021.3086796","title":"Hyperspectral Classification Based on Coupling Multiscale Super-Pixels and Spatial Spectral Features","display_name":"Hyperspectral Classification Based on Coupling Multiscale Super-Pixels and Spatial Spectral Features","publication_year":2021,"publication_date":"2021-06-28","ids":{"openalex":"https://openalex.org/W3174003606","doi":"https://doi.org/10.1109/lgrs.2021.3086796","mag":"3174003606"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2021.3086796","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2021.3086796","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/A5100403908","display_name":"Hua Wang","orcid":"https://orcid.org/0000-0001-6703-6882"},"institutions":[{"id":"https://openalex.org/I23171815","display_name":"Zhengzhou University of Light Industry","ror":"https://ror.org/05fwr8z16","country_code":"CN","type":"education","lineage":["https://openalex.org/I23171815"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hua Wang","raw_affiliation_strings":["School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-6703-6882","affiliations":[{"raw_affiliation_string":"School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China","institution_ids":["https://openalex.org/I23171815"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101957793","display_name":"Weiwei Li","orcid":"https://orcid.org/0000-0003-2157-9261"},"institutions":[{"id":"https://openalex.org/I23171815","display_name":"Zhengzhou University of Light Industry","ror":"https://ror.org/05fwr8z16","country_code":"CN","type":"education","lineage":["https://openalex.org/I23171815"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwei Li","raw_affiliation_strings":["School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-2157-9261","affiliations":[{"raw_affiliation_string":"School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China","institution_ids":["https://openalex.org/I23171815"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101718027","display_name":"Xueye Chen","orcid":"https://orcid.org/0009-0009-1194-6399"},"institutions":[{"id":"https://openalex.org/I211433327","display_name":"Ministry of Natural Resources","ror":"https://ror.org/02kxqx159","country_code":"CN","type":"government","lineage":["https://openalex.org/I211433327","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueye Chen","raw_affiliation_strings":["Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen, China","institution_ids":["https://openalex.org/I211433327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023128300","display_name":"Jiqiang Niu","orcid":"https://orcid.org/0000-0002-3315-2970"},"institutions":[{"id":"https://openalex.org/I130750295","display_name":"Xinyang Normal University","ror":"https://ror.org/0190x2a66","country_code":"CN","type":"education","lineage":["https://openalex.org/I130750295"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiqiang Niu","raw_affiliation_strings":["Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, China","institution_ids":["https://openalex.org/I130750295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6327,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.72142815,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"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":0.9998999834060669,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9915000200271606,"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":0.9465000033378601,"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":0.8276687860488892},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7181209325790405},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7055754661560059},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6655693650245667},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6514351963996887},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.5192484855651855},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5145177841186523},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5144548416137695},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5078039765357971},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.46040523052215576},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.420674204826355},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4153881371021271},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3375401794910431},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3205335736274719},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.2906990051269531},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23783689737319946},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.13453763723373413}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8276687860488892},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7181209325790405},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7055754661560059},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6655693650245667},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6514351963996887},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.5192484855651855},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5145177841186523},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5144548416137695},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5078039765357971},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.46040523052215576},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.420674204826355},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4153881371021271},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3375401794910431},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3205335736274719},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2906990051269531},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23783689737319946},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.13453763723373413},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2021.3086796","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2021.3086796","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":[{"score":0.4399999976158142,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[{"id":"https://openalex.org/G7037264271","display_name":null,"funder_award_id":"41771438","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/W2111787810","https://openalex.org/W2179290474","https://openalex.org/W2373523947","https://openalex.org/W2772452219","https://openalex.org/W2887487859","https://openalex.org/W2892803305","https://openalex.org/W2909295255","https://openalex.org/W3008856558","https://openalex.org/W3044672218","https://openalex.org/W3083760513","https://openalex.org/W3091030342","https://openalex.org/W3134490757","https://openalex.org/W3159090441","https://openalex.org/W4385527727"],"related_works":["https://openalex.org/W3173596272","https://openalex.org/W2546942002","https://openalex.org/W3131722669","https://openalex.org/W2983327997","https://openalex.org/W2940977206","https://openalex.org/W2738461075","https://openalex.org/W2146204105","https://openalex.org/W3005023910","https://openalex.org/W4327773867","https://openalex.org/W2148791530"],"abstract_inverted_index":{"Hyperspectral":[0],"remotely":[1,63],"sensed":[2,64],"images":[3,26,65],"contain":[4],"not":[5],"only":[6],"the":[7,21,28,34,46,59,94,111,121,132,140,169,183,188,202,217,235],"spatial":[8,135],"information":[9,95],"of":[10,24,42,50,61,77,124,157,219,228,238],"ground":[11],"objects,":[12],"but":[13],"also":[14],"their":[15],"rich":[16],"spectral":[17,136,142],"information.":[18],"Effectively":[19],"improve":[20,234],"classification":[22,60,103,185,236],"accuracy":[23,237],"hyperspectral":[25,62],"(HSIs),":[27],"purpose":[29],"is":[30,41],"to":[31,45,109,115,130,144,172],"accurately":[32],"grasp":[33],"current":[35],"land":[36,52],"resource":[37],"utilization":[38],"information,":[39],"which":[40,99],"great":[43],"significance":[44],"formulation":[47],"and":[48,53,87,129,162,174,198,224,231],"implementation":[49],"future":[51],"space":[54],"planning.":[55],"Existing":[56],"research":[57],"on":[58,120],"uses":[66],"a":[67,88,146],"single-scale":[68],"superpixel":[69,112,118,134],"method":[70,114,194,213],"for":[71,151,187],"image":[72,82,221],"segmentation.":[73],"The":[74,154,178,208],"optimal":[75],"number":[76,227],"superpixels":[78,230],"cannot":[79,92],"be":[80],"determined,":[81],"details":[83],"may":[84],"get":[85],"missed,":[86],"single":[89],"kernel":[90,137,143,148],"matrix":[91],"represent":[93],"from":[96],"multiple":[97,127],"features,":[98],"results":[100,180,209],"in":[101],"reduced":[102],"accuracy.":[104],"Therefore,":[105],"this":[106,176,193,212],"study":[107],"intends":[108],"use":[110],"segmentation":[113,119],"perform":[116],"multiscale":[117,133],"principal":[122],"components":[123],"HSIs":[125,156],"at":[126,206],"scales,":[128],"couple":[131],"(SSK)":[138],"with":[139],"original":[141],"form":[145],"synthetic":[147],"using":[149],"weights":[150],"HSI":[152],"classification.":[153],"three":[155,189],"Pavia":[158,160],"University,":[159],"Center,":[161],"Washington":[163],"DC":[164],"Mall":[165],"are":[166,195],"used":[167],"as":[168],"experimental":[170,179],"data":[171],"test":[173],"analyze":[175],"method.":[177],"show":[181],"that":[182,211],"effective":[184],"accuracies":[186],"datasets":[190],"obtained":[191],"by":[192],"5.28%,":[196],"5.90%,":[197],"7.71%":[199],"higher":[200],"than":[201],"five":[203],"comparison":[204],"methods,":[205],"best.":[207],"prove":[210],"can":[214,232],"effectively":[215],"solve":[216],"problem":[218],"imprecise":[220],"feature":[222],"extraction":[223],"an":[225],"unknown":[226],"initial":[229],"significantly":[233],"HSIs.":[239]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
