{"id":"https://openalex.org/W3131404269","doi":"https://doi.org/10.1109/igarss39084.2020.9324170","title":"Multiscale Convolution Network with Region-Based Max Voting for Hyprrsprctral Imagrs Classificatton","display_name":"Multiscale Convolution Network with Region-Based Max Voting for Hyprrsprctral Imagrs Classificatton","publication_year":2020,"publication_date":"2020-09-26","ids":{"openalex":"https://openalex.org/W3131404269","doi":"https://doi.org/10.1109/igarss39084.2020.9324170","mag":"3131404269"},"language":"en","primary_location":{"id":"doi:10.1109/igarss39084.2020.9324170","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9324170","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 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/A5101720039","display_name":"Xuming Zhang","orcid":"https://orcid.org/0000-0003-4332-071X"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuming Zhang","raw_affiliation_strings":["College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, Shandong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, Shandong, China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074984354","display_name":"Aizhu Zhang","orcid":"https://orcid.org/0000-0003-2226-8908"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aizhu Zhang","raw_affiliation_strings":["College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, Shandong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, Shandong, China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053518414","display_name":"Genyun Sun","orcid":"https://orcid.org/0000-0002-2641-2615"},"institutions":[{"id":"https://openalex.org/I4210113896","display_name":"Qingdao National Laboratory for Marine Science and Technology","ror":"https://ror.org/026sv7t11","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210113896"]},{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Genyun Sun","raw_affiliation_strings":["College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, Shandong, China","Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, Shandong, China","institution_ids":["https://openalex.org/I4210162190"]},{"raw_affiliation_string":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China","institution_ids":["https://openalex.org/I4210113896"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102198875","display_name":"Yanjuan Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210158498","display_name":"Satellite Application Center for Ecology and Environment","ror":"https://ror.org/04x9p9g72","country_code":"CN","type":"government","lineage":["https://openalex.org/I204710742","https://openalex.org/I4210158498"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanjuan Yao","raw_affiliation_strings":["Satellite Environment Center, Ministry of Environmental protection of China, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Satellite Environment Center, Ministry of Environmental protection of China, Beijing, China","institution_ids":["https://openalex.org/I4210158498"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0847,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.77295329,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"11","issue":null,"first_page":"64","last_page":"67"},"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":0.996399998664856,"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":0.9878000020980835,"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/discriminative-model","display_name":"Discriminative model","score":0.7548574805259705},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7329158782958984},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7274831533432007},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7002622485160828},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6356881260871887},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6187986135482788},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5404459238052368},{"id":"https://openalex.org/keywords/voting","display_name":"Voting","score":0.49276235699653625},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46619147062301636},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4310571551322937},{"id":"https://openalex.org/keywords/weighted-voting","display_name":"Weighted voting","score":0.4170324206352234},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.41544854640960693},{"id":"https://openalex.org/keywords/majority-rule","display_name":"Majority rule","score":0.4125324785709381},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.34244462847709656},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.29647642374038696}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7548574805259705},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7329158782958984},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7274831533432007},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7002622485160828},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6356881260871887},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6187986135482788},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5404459238052368},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.49276235699653625},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46619147062301636},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4310571551322937},{"id":"https://openalex.org/C132778050","wikidata":"https://www.wikidata.org/wiki/Q2065430","display_name":"Weighted voting","level":4,"score":0.4170324206352234},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.41544854640960693},{"id":"https://openalex.org/C153668964","wikidata":"https://www.wikidata.org/wiki/Q27636","display_name":"Majority rule","level":2,"score":0.4125324785709381},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34244462847709656},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.29647642374038696},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss39084.2020.9324170","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9324170","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2056302425","https://openalex.org/W2131864940","https://openalex.org/W2132648706","https://openalex.org/W2140991832","https://openalex.org/W2166923144","https://openalex.org/W2171224700","https://openalex.org/W2314785379","https://openalex.org/W2412782625","https://openalex.org/W2558391528","https://openalex.org/W2765739551","https://openalex.org/W2765939201","https://openalex.org/W2766988648","https://openalex.org/W2809918877","https://openalex.org/W2901117552","https://openalex.org/W2916206107","https://openalex.org/W6745413831"],"related_works":["https://openalex.org/W2782869875","https://openalex.org/W2970216048","https://openalex.org/W2285052147","https://openalex.org/W3173596272","https://openalex.org/W2725397116","https://openalex.org/W2806866760","https://openalex.org/W2955261746","https://openalex.org/W3043181422","https://openalex.org/W3026401485","https://openalex.org/W2406522397"],"abstract_inverted_index":{"Feature":[0],"extraction":[1,15],"is":[2,41,52,76,92,116],"of":[3],"significance":[4],"for":[5,82,118,122],"hyperspectral":[6],"image":[7],"(HSI)":[8],"classification.":[9,38,65],"Compared":[10],"with":[11,26,124],"conventional":[12],"handcrafted":[13],"feature":[14],"methods,":[16],"convolutional":[17],"neural":[18],"network":[19],"(CNN)":[20],"can":[21],"automatically":[22],"learn":[23],"hierarchical":[24],"features":[25,81],"discriminative":[27],"information.":[28],"However,":[29],"two":[30,107],"issues":[31],"exist":[32],"in":[33,70],"applying":[34],"CNN":[35,64,75],"to":[36,43,54,78,94,98],"HSI":[37,83,119],"One":[39],"issue":[40],"how":[42,53],"represent":[44],"the":[45,50,56,95,100,113],"land":[46],"covers":[47],"at":[48],"multiscale,":[49],"other":[51],"solve":[55,67,99],"\u201csalt":[57,101],"and":[58,85,102],"pepper\u201d":[59,103],"noises":[60],"caused":[61],"by":[62],"pixel-based":[63],"To":[66],"these":[68],"issues,":[69],"this":[71],"paper,":[72],"a":[73,87],"multiscale":[74,80],"proposed":[77,114],"extract":[79],"classification,":[84,120],"then":[86],"region-based":[88],"max":[89],"voting":[90],"scheme":[91],"applied":[93],"classification":[96],"map":[97],"noises.":[104],"Experiments":[105],"on":[106],"classical":[108],"data":[109],"sets":[110],"demonstrate":[111],"that":[112],"method":[115],"effective":[117],"especially":[121],"images":[123],"large":[125],"scale":[126],"changes.":[127]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
