{"id":"https://openalex.org/W2947058136","doi":"https://doi.org/10.1109/jstars.2019.2915272","title":"Noise-Robust Hyperspectral Image Classification via Multi-Scale Total Variation","display_name":"Noise-Robust Hyperspectral Image Classification via Multi-Scale Total Variation","publication_year":2019,"publication_date":"2019-05-29","ids":{"openalex":"https://openalex.org/W2947058136","doi":"https://doi.org/10.1109/jstars.2019.2915272","mag":"2947058136"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2019.2915272","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstars.2019.2915272","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":false,"is_in_doaj":true,"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"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/A5011401698","display_name":"Puhong Duan","orcid":"https://orcid.org/0000-0001-5066-4399"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Puhong Duan","raw_affiliation_strings":["Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0001-5066-4399","affiliations":[{"raw_affiliation_string":"Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province, Changsha, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057514965","display_name":"Xudong Kang","orcid":"https://orcid.org/0000-0002-3807-2531"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xudong Kang","raw_affiliation_strings":["Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-3807-2531","affiliations":[{"raw_affiliation_string":"Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province, Changsha, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067097659","display_name":"Shutao Li","orcid":"https://orcid.org/0000-0002-0585-9848"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shutao Li","raw_affiliation_strings":["Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-0585-9848","affiliations":[{"raw_affiliation_string":"Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province, Changsha, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074919292","display_name":"Pedram Ghamisi","orcid":"https://orcid.org/0000-0003-1203-741X"},"institutions":[{"id":"https://openalex.org/I2801798921","display_name":"Helmholtz-Zentrum Dresden-Rossendorf","ror":"https://ror.org/01zy2cs03","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I2801798921"]},{"id":"https://openalex.org/I4210148560","display_name":"Helmholtz Institute Freiberg for Resource Technology","ror":"https://ror.org/04kdb0j04","country_code":"DE","type":"government","lineage":["https://openalex.org/I1305996414","https://openalex.org/I2801798921","https://openalex.org/I4210148560"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Pedram Ghamisi","raw_affiliation_strings":["Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Dresden, Germany"],"raw_orcid":"https://orcid.org/0000-0003-1203-741X","affiliations":[{"raw_affiliation_string":"Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Dresden, Germany","institution_ids":["https://openalex.org/I2801798921","https://openalex.org/I4210148560"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":null,"fwci":10.6907,"has_fulltext":false,"cited_by_count":102,"citation_normalized_percentile":{"value":0.98517408,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"12","issue":"6","first_page":"1948","last_page":"1962"},"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":0.9955999851226807,"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":0.9846000075340271,"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":0.8225461840629578},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7928513884544373},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.7217541337013245},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7214046716690063},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6756429672241211},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5659838318824768},{"id":"https://openalex.org/keywords/kernel-principal-component-analysis","display_name":"Kernel principal component analysis","score":0.5464913845062256},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5131005048751831},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4970727264881134},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.44523343443870544},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4087855815887451},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.356453001499176},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.26668763160705566},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.19513744115829468}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8225461840629578},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7928513884544373},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.7217541337013245},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7214046716690063},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6756429672241211},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5659838318824768},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.5464913845062256},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5131005048751831},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4970727264881134},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44523343443870544},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4087855815887451},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.356453001499176},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.26668763160705566},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.19513744115829468},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jstars.2019.2915272","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstars.2019.2915272","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":false,"is_in_doaj":true,"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2027762821","display_name":null,"funder_award_id":"61890962","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6644882702","display_name":null,"funder_award_id":"6187119","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8694044906","display_name":null,"funder_award_id":"61601179","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":68,"referenced_works":["https://openalex.org/W262970172","https://openalex.org/W1535602073","https://openalex.org/W1939429412","https://openalex.org/W1974438823","https://openalex.org/W1979730959","https://openalex.org/W1980849288","https://openalex.org/W1985242206","https://openalex.org/W1985973695","https://openalex.org/W2004104348","https://openalex.org/W2005106632","https://openalex.org/W2016819955","https://openalex.org/W2018482939","https://openalex.org/W2040378863","https://openalex.org/W2041100636","https://openalex.org/W2043469084","https://openalex.org/W2049003564","https://openalex.org/W2052160904","https://openalex.org/W2053615857","https://openalex.org/W2057688677","https://openalex.org/W2070452328","https://openalex.org/W2078718577","https://openalex.org/W2095975892","https://openalex.org/W2101711129","https://openalex.org/W2103094532","https://openalex.org/W2106277226","https://openalex.org/W2107799335","https://openalex.org/W2114819256","https://openalex.org/W2125027820","https://openalex.org/W2125188192","https://openalex.org/W2127199143","https://openalex.org/W2127808402","https://openalex.org/W2136251662","https://openalex.org/W2141957843","https://openalex.org/W2153663612","https://openalex.org/W2158400785","https://openalex.org/W2159070926","https://openalex.org/W2162698522","https://openalex.org/W2166923144","https://openalex.org/W2171520281","https://openalex.org/W2244003928","https://openalex.org/W2345118402","https://openalex.org/W2346557146","https://openalex.org/W2464748116","https://openalex.org/W2496621835","https://openalex.org/W2519956323","https://openalex.org/W2585357012","https://openalex.org/W2592962403","https://openalex.org/W2607215444","https://openalex.org/W2740976805","https://openalex.org/W2743255627","https://openalex.org/W2754507318","https://openalex.org/W2761917471","https://openalex.org/W2779054585","https://openalex.org/W2782517596","https://openalex.org/W2783165089","https://openalex.org/W2799870441","https://openalex.org/W2809635958","https://openalex.org/W2884158490","https://openalex.org/W2885776085","https://openalex.org/W2887785636","https://openalex.org/W2894165434","https://openalex.org/W2896847173","https://openalex.org/W2912961521","https://openalex.org/W3100245404","https://openalex.org/W3103856189","https://openalex.org/W3169247442","https://openalex.org/W3174034775","https://openalex.org/W6609634455"],"related_works":["https://openalex.org/W2379488555","https://openalex.org/W2753886092","https://openalex.org/W2152632846","https://openalex.org/W1992961908","https://openalex.org/W2014683590","https://openalex.org/W3138125914","https://openalex.org/W2359742711","https://openalex.org/W4404440911","https://openalex.org/W2139392257","https://openalex.org/W4285789115"],"abstract_inverted_index":{"In":[0],"this":[1,113],"paper,":[2],"a":[3,53,95],"novel":[4],"multi-scale":[5,40],"total":[6,55],"variation":[7],"method":[8,121],"is":[9,32,130],"proposed":[10,120],"to":[11,47,74,122],"extract":[12],"structural":[13,41],"features":[14,42],"from":[15,99],"hyperspectral":[16,87],"images":[17],"(HSIs),":[18],"which":[19,44],"consists":[20],"of":[21,29,118],"the":[22,26,30,39,61,76,104,116,119,123],"following":[23],"steps.":[24],"First,":[25],"spectral":[27],"dimension":[28],"HSI":[31],"reduced":[33],"with":[34,52],"an":[35,100],"averaging-based":[36],"method.":[37],"Then,":[38],"(MSFs),":[43],"are":[45,50,63],"insensitive":[46],"image":[48,128],"noise,":[49],"constructed":[51],"relative":[54],"variation-based":[56],"structure":[57],"extraction":[58],"technique.":[59],"Finally,":[60],"MSFs":[62,78],"fused":[64],"together":[65],"using":[66],"kernel":[67],"principal":[68],"component":[69],"analysis":[70],"(KPCA),":[71],"so":[72],"as":[73],"obtain":[75],"KPCA-fused":[77],"for":[79],"classification.":[80],"Experimental":[81],"results":[82],"on":[83],"three":[84],"publicly":[85],"available":[86],"datasets,":[88],"including":[89],"both":[90],"well-known,":[91],"long-used":[92],"data,":[93],"and":[94,126],"recent":[96],"dataset":[97],"obtained":[98],"international":[101],"contest,":[102],"demonstrate":[103],"competitive":[105],"performance":[106],"over":[107],"several":[108],"state-of-the-art":[109],"classification":[110],"approaches":[111],"in":[112],"field.":[114],"Moreover,":[115],"robustness":[117],"small-sample-size":[124],"problem":[125],"serious":[127],"noise":[129],"also":[131],"demonstrated.":[132]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":19},{"year":2021,"cited_by_count":23},{"year":2020,"cited_by_count":22},{"year":2019,"cited_by_count":10}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}