{"id":"https://openalex.org/W4210379926","doi":"https://doi.org/10.1109/tgrs.2022.3146803","title":"Band Sampling of Kernel Constrained Energy Minimization Using Training Samples for Hyperspectral Mixed Pixel Classification","display_name":"Band Sampling of Kernel Constrained Energy Minimization Using Training Samples for Hyperspectral Mixed Pixel Classification","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4210379926","doi":"https://doi.org/10.1109/tgrs.2022.3146803"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3146803","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3146803","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","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/A5073412670","display_name":"Chein\u2010I Chang","orcid":"https://orcid.org/0000-0002-5450-4891"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]},{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Chein-I Chang","raw_affiliation_strings":["Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information and Technology College, Dalian Maritime University, Dalian, China","Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore, MD, USA"],"raw_orcid":"https://orcid.org/0000-0002-5450-4891","affiliations":[{"raw_affiliation_string":"Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information and Technology College, Dalian Maritime University, Dalian, China","institution_ids":["https://openalex.org/I43313876"]},{"raw_affiliation_string":"Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066795807","display_name":"Kenneth Yeonkong","orcid":"https://orcid.org/0000-0003-3636-5311"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kenneth Yeonkong Ma","raw_affiliation_strings":["Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore County, Baltimore, MD, USA"],"raw_orcid":"https://orcid.org/0000-0003-3636-5311","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0974,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.78920542,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"21"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9988999962806702,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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":0.8148305416107178},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5999189615249634},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5908755660057068},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5833238959312439},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.5809699296951294},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5465043783187866},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5012364387512207},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4927118122577667},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4490557014942169},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.2415618896484375},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18669918179512024},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.1122545599937439}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8148305416107178},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5999189615249634},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5908755660057068},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5833238959312439},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.5809699296951294},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5465043783187866},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5012364387512207},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4927118122577667},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4490557014942169},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2415618896484375},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18669918179512024},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.1122545599937439},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2022.3146803","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3146803","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8999999761581421,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G1447177257","display_name":null,"funder_award_id":"3132019341","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1899348529","https://openalex.org/W2049189005","https://openalex.org/W2053615857","https://openalex.org/W2101252963","https://openalex.org/W2104266187","https://openalex.org/W2115927538","https://openalex.org/W2119667497","https://openalex.org/W2138038253","https://openalex.org/W2154236340","https://openalex.org/W2165755981","https://openalex.org/W2166923144","https://openalex.org/W2316226477","https://openalex.org/W2493273699","https://openalex.org/W2743091961","https://openalex.org/W2782781942","https://openalex.org/W2791725003","https://openalex.org/W2899937451","https://openalex.org/W2901920630","https://openalex.org/W2907064575","https://openalex.org/W2948763898","https://openalex.org/W2950325582","https://openalex.org/W3021090805","https://openalex.org/W3039508690","https://openalex.org/W3041591456","https://openalex.org/W3087883793","https://openalex.org/W3127327694","https://openalex.org/W3156696558","https://openalex.org/W3175968366","https://openalex.org/W3184375104","https://openalex.org/W3193418296","https://openalex.org/W3195212285","https://openalex.org/W3210780861","https://openalex.org/W3214045049","https://openalex.org/W4248253651","https://openalex.org/W4250955649","https://openalex.org/W6676930901"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2404757046","https://openalex.org/W2044184146","https://openalex.org/W2070598848","https://openalex.org/W4313014865","https://openalex.org/W2076134148"],"abstract_inverted_index":{"Constrained":[0],"energy":[1],"minimization":[2],"(CEM)":[3],"has":[4],"been":[5],"extended":[6],"to":[7,42,63,98],"several":[8],"generalized":[9],"versions,":[10],"iterative":[11,20],"CEM":[12,17,31,155],"(ICEM),":[13],"Nystrom":[14],"method-based":[15],"kernel":[16],"(NKCEM),":[18],"and":[19,114,127,156,162],"training":[21,142],"sampling-based":[22],"NKCEM":[23,65,113,125],"(ITS-NKCEM)":[24],"for":[25,72,107,120],"hyperspectral":[26,73],"image":[27],"classification":[28,76],"(HSIC).":[29],"Since":[30],"is":[32],"a":[33,39,67,79],"subpixel":[34,61],"target":[35,40,45],"detector":[36],"that":[37,134,147,169],"specifies":[38],"signature":[41],"detect":[43],"its":[44,100],"abundance":[46],"fractions":[47],"present":[48],"in":[49,60],"data":[50],"samples,":[51,143],"this":[52],"article":[53],"takes":[54],"the":[55],"advantage":[56],"of":[57,82],"CEM\u2019s":[58],"ability":[59],"detection":[62],"consider":[64],"as":[66,139,141],"nonlinear":[68],"mixed":[69,74],"pixel":[70,75],"classifier":[71],"(HMPC).":[77],"Recently,":[78],"new":[80,118],"concept":[81],"band":[83,104,123,128],"sampling":[84,92,124,129],"(BSam)":[85],"was":[86],"proposed":[87],"by":[88],"utilizing":[89],"random":[90],"signal":[91],"derived":[93],"from":[94],"compressive":[95],"sensing":[96],"(CS)":[97],"show":[99],"performance":[101],"better":[102,151,173],"than":[103,152,174],"selection":[105],"(BSel)":[106],"HSIC.":[108],"Thus,":[109],"incorporating":[110],"BSam":[111],"into":[112],"ITS-NKCEM":[115,130],"yields":[116],"two":[117],"versions":[119,158],"HPMC,":[121],"called":[122],"(BSam-NKCEM)":[126],"(BSam-ITS-NKCEM).":[131],"Interestingly,":[132],"despite":[133],"BSam-ITS-NKCEM":[135,170],"uses":[136],"sampled":[137],"bands":[138,161],"well":[140],"extensive":[144],"experiments":[145],"demonstrate":[146],"it":[148,166],"can":[149,171],"perform":[150],"all":[153],"other":[154],"KCEM":[157],"using":[159,178],"full":[160],"ground":[163],"truth.":[164],"Specifically,":[165],"also":[167],"shows":[168],"do":[172],"spectral\u2013spatial":[175],"HSIC":[176],"techniques":[177],"BSel.":[179]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
