{"id":"https://openalex.org/W2082077200","doi":"https://doi.org/10.1109/jstars.2014.2307579","title":"Improving the Dynamic Clustering of Hyperspectral Data Based on the Integration of Swarm Optimization and Decision Analysis","display_name":"Improving the Dynamic Clustering of Hyperspectral Data Based on the Integration of Swarm Optimization and Decision Analysis","publication_year":2014,"publication_date":"2014-03-19","ids":{"openalex":"https://openalex.org/W2082077200","doi":"https://doi.org/10.1109/jstars.2014.2307579","mag":"2082077200"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2014.2307579","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstars.2014.2307579","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/A5067583353","display_name":"Amin Alizadeh Naeini","orcid":"https://orcid.org/0000-0001-7578-6245"},"institutions":[{"id":"https://openalex.org/I23946033","display_name":"University of Tehran","ror":"https://ror.org/05vf56z40","country_code":"IR","type":"education","lineage":["https://openalex.org/I23946033"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Amin Alizadeh Naeini","raw_affiliation_strings":["Department of Geomatics, University of Tehran, Tehran, Iran","Department of Geomatics, College of Engineering, University of Tehran, Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"Department of Geomatics, University of Tehran, Tehran, Iran","institution_ids":["https://openalex.org/I23946033"]},{"raw_affiliation_string":"Department of Geomatics, College of Engineering, University of Tehran, Tehran, Iran","institution_ids":["https://openalex.org/I23946033"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063724972","display_name":"Saeid Homayouni","orcid":"https://orcid.org/0000-0002-0214-5356"},"institutions":[{"id":"https://openalex.org/I153718931","display_name":"University of Ottawa","ror":"https://ror.org/03c4mmv16","country_code":"CA","type":"education","lineage":["https://openalex.org/I153718931"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Saeid Homayouni","raw_affiliation_strings":["Department of Geography, University of Ottawa, Ottawa, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Geography, University of Ottawa, Ottawa, Canada","institution_ids":["https://openalex.org/I153718931"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023819067","display_name":"M. Saadatseresht","orcid":"https://orcid.org/0000-0002-7918-3166"},"institutions":[{"id":"https://openalex.org/I23946033","display_name":"University of Tehran","ror":"https://ror.org/05vf56z40","country_code":"IR","type":"education","lineage":["https://openalex.org/I23946033"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Mohammad Saadatseresht","raw_affiliation_strings":["Department of Geomatics, University of Tehran, Tehran, Iran","Department of Geomatics, College of Engineering, University of Tehran, Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"Department of Geomatics, University of Tehran, Tehran, Iran","institution_ids":["https://openalex.org/I23946033"]},{"raw_affiliation_string":"Department of Geomatics, College of Engineering, University of Tehran, Tehran, Iran","institution_ids":["https://openalex.org/I23946033"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5067583353"],"corresponding_institution_ids":["https://openalex.org/I23946033"],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":null,"fwci":3.1365,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.92524217,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"7","issue":"6","first_page":"2161","last_page":"2173"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9995999932289124,"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":0.9995999932289124,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9789999723434448,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9710000157356262,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8325764536857605},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6782193183898926},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.6190901398658752},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5898674726486206},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.584289014339447},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.5778480768203735},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.5455330610275269},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.47600802779197693},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46899914741516113},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.466656893491745},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.44976502656936646},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.44685134291648865},{"id":"https://openalex.org/keywords/constrained-clustering","display_name":"Constrained clustering","score":0.44676411151885986},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.41475972533226013},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.4126371741294861},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.41246867179870605},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.23353305459022522}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8325764536857605},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6782193183898926},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.6190901398658752},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5898674726486206},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.584289014339447},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.5778480768203735},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.5455330610275269},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.47600802779197693},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46899914741516113},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.466656893491745},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.44976502656936646},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.44685134291648865},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.44676411151885986},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.41475972533226013},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.4126371741294861},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.41246867179870605},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.23353305459022522}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jstars.2014.2307579","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstars.2014.2307579","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":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6200000047683716}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W120094446","https://openalex.org/W129903785","https://openalex.org/W152643988","https://openalex.org/W1547841921","https://openalex.org/W1551664459","https://openalex.org/W1587294773","https://openalex.org/W1600188168","https://openalex.org/W1838818075","https://openalex.org/W1984152986","https://openalex.org/W1986813400","https://openalex.org/W1988698908","https://openalex.org/W1992419399","https://openalex.org/W2008043556","https://openalex.org/W2010319424","https://openalex.org/W2011430131","https://openalex.org/W2012468319","https://openalex.org/W2012558304","https://openalex.org/W2017782250","https://openalex.org/W2021198620","https://openalex.org/W2046926583","https://openalex.org/W2063682792","https://openalex.org/W2065109455","https://openalex.org/W2071949631","https://openalex.org/W2075075331","https://openalex.org/W2076566738","https://openalex.org/W2077375588","https://openalex.org/W2092363223","https://openalex.org/W2097612618","https://openalex.org/W2098477891","https://openalex.org/W2099248875","https://openalex.org/W2107903523","https://openalex.org/W2113156691","https://openalex.org/W2116854683","https://openalex.org/W2123890255","https://openalex.org/W2125213524","https://openalex.org/W2125799222","https://openalex.org/W2132549764","https://openalex.org/W2134663338","https://openalex.org/W2143177983","https://openalex.org/W2143416511","https://openalex.org/W2147680910","https://openalex.org/W2148669440","https://openalex.org/W2153540854","https://openalex.org/W2154261973","https://openalex.org/W2163383000","https://openalex.org/W2165049595","https://openalex.org/W2165187708","https://openalex.org/W2169569314","https://openalex.org/W2351302360","https://openalex.org/W2406093411","https://openalex.org/W2489066590","https://openalex.org/W2609003788","https://openalex.org/W2798741797","https://openalex.org/W4230073927","https://openalex.org/W4237280149","https://openalex.org/W6635736912","https://openalex.org/W6638913232","https://openalex.org/W6682662355","https://openalex.org/W6684679353","https://openalex.org/W6736940977"],"related_works":["https://openalex.org/W4301002638","https://openalex.org/W3088133960","https://openalex.org/W1525022337","https://openalex.org/W2160785859","https://openalex.org/W1979094538","https://openalex.org/W2393707058","https://openalex.org/W3146523624","https://openalex.org/W3124860551","https://openalex.org/W3186815950","https://openalex.org/W2202413591"],"abstract_inverted_index":{"Unsupervised":[0],"or":[1],"clustering":[2,31,39,83,92,179],"algorithms":[3,28,154],"can":[4,77],"be":[5,78],"considered":[6],"to":[7,131,145,188],"overcome":[8],"the":[9,24,30,44,51,72,99,102,123,140,147,157,165,174],"need":[10],"for":[11,18,29,125,181],"both":[12],"high-quantity":[13],"and":[14,59,74,96,168],"high-quality":[15],"training":[16],"data":[17,20,34],"hyperspectral":[19,182],"classification.":[21],"One":[22],"of":[23,32,47,53,63,105,127,143,170],"most":[25],"widely":[26],"used":[27],"remotely-sensed":[33],"is":[35,40,94,107,137,160],"partitional":[36],"clustering.":[37],"Partitional":[38],"affected":[41],"by":[42,81,109,129],"1)":[43],"optimal":[45,141,166],"number":[46,167],"clusters":[48],"(NOC),":[49],"2)":[50],"position":[52,169],"cluster":[54],"centers":[55],"in":[56],"hyper-dimension":[57],"space,":[58],"3)":[60],"a":[61,110,135,189],"set":[62,104,142],"optimally":[64],"discriminating":[65],"spectral":[66],"bands.":[67],"Among":[68],"these":[69],"three":[70],"parameters,":[71],"NOC":[73],"their":[75],"positions":[76],"found":[79],"simultaneously":[80],"dynamic":[82,91],"approaches.":[84],"In":[85,98,172],"this":[86],"paper,":[87],"an":[88,117],"innovative":[89],"two-stage":[90],"method":[93,159,186],"proposed":[95,158,175],"evaluated.":[97],"first":[100],"stage,":[101],"optimum":[103],"solutions":[106,144],"achieved":[108],"multi-objective":[111],"particle":[112],"swarm":[113],"optimization.":[114],"Then,":[115],"using":[116],"efficient":[118],"multi-criteria":[119],"decision-making":[120],"method,":[121],"namely,":[122],"technique":[124],"order":[126],"preference":[128],"similarity":[130],"ideal":[132],"solution":[133],"(TOPSIS),":[134],"ranking":[136],"done":[138],"among":[139],"select":[146],"best":[148],"one.":[149],"Comparisons":[150],"with":[151],"some":[152],"classic":[153],"reveal":[155],"that":[156],"more":[161],"effective":[162],"at":[163],"detecting":[164],"clusters.":[171],"addition,":[173],"algorithm":[176],"generates":[177],"better":[178],"results":[180],"data.":[183],"Indeed,":[184],"our":[185],"leads":[187],"5%-10%":[190],"improvement":[191],"upon":[192],"classification":[193],"accuracy.":[194]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
