{"id":"https://openalex.org/W3039508690","doi":"https://doi.org/10.1109/lgrs.2020.3005078","title":"An Iterative Random Training Sample Selection Approach to Constrained Energy Minimization for Hyperspectral Image Classification","display_name":"An Iterative Random Training Sample Selection Approach to Constrained Energy Minimization for Hyperspectral Image Classification","publication_year":2020,"publication_date":"2020-07-02","ids":{"openalex":"https://openalex.org/W3039508690","doi":"https://doi.org/10.1109/lgrs.2020.3005078","mag":"3039508690"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2020.3005078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3005078","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/A5019546610","display_name":"Xiaodi Shang","orcid":"https://orcid.org/0000-0002-0133-8447"},"institutions":[{"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"],"is_corresponding":false,"raw_author_name":"Xiaodi Shang","raw_affiliation_strings":["Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information and Technology College, Dalian Maritime University, Dalian, China"],"raw_orcid":"https://orcid.org/0000-0002-0133-8447","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"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101405735","display_name":"Meiping Song","orcid":"https://orcid.org/0000-0002-4489-5470"},"institutions":[{"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"],"is_corresponding":false,"raw_author_name":"Meiping Song","raw_affiliation_strings":["Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information and Technology College, Dalian Maritime University, Dalian, China"],"raw_orcid":null,"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"]}]},{"author_position":"last","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/I177918364","display_name":"Providence University","ror":"https://ror.org/03fcpsq87","country_code":"TW","type":"education","lineage":["https://openalex.org/I177918364"]}],"countries":["TW","US"],"is_corresponding":false,"raw_author_name":"Chein-I Chang","raw_affiliation_strings":["Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore, MD, USA","Department of Computer Science and Information Management, Providence University, Taichung, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-5450-4891","affiliations":[{"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"]},{"raw_affiliation_string":"Department of Computer Science and Information Management, Providence University, Taichung, Taiwan","institution_ids":["https://openalex.org/I177918364"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.7816,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.94166246,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"18","issue":"9","first_page":"1625","last_page":"1629"},"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":0.9890999794006348,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.699204683303833},{"id":"https://openalex.org/keywords/iterative-method","display_name":"Iterative method","score":0.6987979412078857},{"id":"https://openalex.org/keywords/iterative-and-incremental-development","display_name":"Iterative and incremental development","score":0.5885990858078003},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5819380283355713},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.577678918838501},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5513411164283752},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5267514586448669},{"id":"https://openalex.org/keywords/energy-minimization","display_name":"Energy minimization","score":0.5251799821853638},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5228429436683655},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.48895180225372314},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.48345717787742615},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4480293095111847},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.410230815410614},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39447134733200073},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3665667176246643},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29590681195259094},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2117747962474823},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.17594298720359802}],"concepts":[{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.699204683303833},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.6987979412078857},{"id":"https://openalex.org/C143587482","wikidata":"https://www.wikidata.org/wiki/Q1543216","display_name":"Iterative and incremental development","level":2,"score":0.5885990858078003},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5819380283355713},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.577678918838501},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5513411164283752},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5267514586448669},{"id":"https://openalex.org/C14961307","wikidata":"https://www.wikidata.org/wiki/Q5377176","display_name":"Energy minimization","level":2,"score":0.5251799821853638},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5228429436683655},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.48895180225372314},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.48345717787742615},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4480293095111847},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.410230815410614},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39447134733200073},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3665667176246643},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29590681195259094},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2117747962474823},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.17594298720359802},{"id":"https://openalex.org/C147597530","wikidata":"https://www.wikidata.org/wiki/Q369472","display_name":"Computational chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","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},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2020.3005078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3005078","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":[{"display_name":"Affordable and clean energy","score":0.8999999761581421,"id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G1455175829","display_name":null,"funder_award_id":"61601077","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G545681976","display_name":null,"funder_award_id":"61890964","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5800071179","display_name":"\u57fa\u4e8e\u6d4b\u5ea6\u4fdd\u6301\u548c\u4efb\u52a1\u9a71\u52a8\u7684\u975e\u76d1\u7763\u6ce2\u6bb5\u9009\u62e9\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61971082","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":18,"referenced_works":["https://openalex.org/W1532610010","https://openalex.org/W2001298023","https://openalex.org/W2106277226","https://openalex.org/W2133059825","https://openalex.org/W2166923144","https://openalex.org/W2743091961","https://openalex.org/W2782051899","https://openalex.org/W2795396578","https://openalex.org/W2799870441","https://openalex.org/W2890306271","https://openalex.org/W2894165434","https://openalex.org/W2901920630","https://openalex.org/W2907064575","https://openalex.org/W2944896556","https://openalex.org/W2947058136","https://openalex.org/W2965091010","https://openalex.org/W2971007343","https://openalex.org/W3021090805"],"related_works":["https://openalex.org/W2065641033","https://openalex.org/W3091585071","https://openalex.org/W2351116219","https://openalex.org/W1483822002","https://openalex.org/W2972785471","https://openalex.org/W2377946372","https://openalex.org/W4210766555","https://openalex.org/W2121257894","https://openalex.org/W3206999869","https://openalex.org/W3039508690"],"abstract_inverted_index":{"Iterative":[0],"constrained":[1,46],"energy":[2,47],"minimization":[3,48],"(ICEM)":[4],"has":[5],"shown":[6],"success":[7],"in":[8],"classification.":[9],"However,":[10],"a":[11,53,72],"drawback":[12],"suffered":[13],"from":[14],"ICEM":[15,38],"is":[16],"its":[17],"requirement":[18],"of":[19,33],"complete":[20],"ground":[21],"truth":[22],"to":[23,36,93],"calculate":[24],"class":[25],"means.":[26],"This":[27],"letter":[28],"develops":[29],"an":[30],"iterative":[31,42,61,64],"selection":[32],"training":[34,44,55,66,74],"samples":[35],"extend":[37],"with":[39],"two":[40],"versions:":[41],"fixed":[43,54],"sampling":[45,67,75],"(CEM)":[49],"(IFTS-CEM)":[50],"which":[51,70],"uses":[52,71],"sample":[56],"set":[57],"throughout":[58],"the":[59],"entire":[60],"process":[62],"and":[63,90],"random":[65,73],"CEM":[68],"(IRTS-CEM)":[69],"(RTS)":[76],"at":[77],"each":[78],"iteration.":[79],"The":[80],"experimental":[81],"results":[82],"demonstrate":[83],"that":[84],"IRTS-CEM":[85],"performs":[86],"better":[87],"than":[88],"IFTS-CEM":[89],"also":[91],"comparable":[92],"ICEM.":[94]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}