{"id":"https://openalex.org/W4206655275","doi":"https://doi.org/10.1109/icct52962.2021.9657977","title":"Performance Evaluation of Machine Learning Classifiers for Hyperspectral Images","display_name":"Performance Evaluation of Machine Learning Classifiers for Hyperspectral Images","publication_year":2021,"publication_date":"2021-10-13","ids":{"openalex":"https://openalex.org/W4206655275","doi":"https://doi.org/10.1109/icct52962.2021.9657977"},"language":"en","primary_location":{"id":"doi:10.1109/icct52962.2021.9657977","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icct52962.2021.9657977","pdf_url":null,"source":{"id":"https://openalex.org/S4363607878","display_name":"2021 IEEE 21st International Conference on Communication Technology (ICCT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 21st International Conference on Communication Technology (ICCT)","raw_type":"proceedings-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/A5029449476","display_name":"H. C. M. Herath","orcid":"https://orcid.org/0000-0001-5256-8552"},"institutions":[{"id":"https://openalex.org/I111199411","display_name":"University of Peradeniya","ror":"https://ror.org/025h79t26","country_code":"LK","type":"education","lineage":["https://openalex.org/I111199411"]}],"countries":["LK"],"is_corresponding":true,"raw_author_name":"H. C. M. Herath","raw_affiliation_strings":["Faculty of Engineering, University of Peradeniya, Sri Lanka"],"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, University of Peradeniya, Sri Lanka","institution_ids":["https://openalex.org/I111199411"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5029449476"],"corresponding_institution_ids":["https://openalex.org/I111199411"],"apc_list":null,"apc_paid":null,"fwci":0.3817,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.55971897,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"62","issue":null,"first_page":"1216","last_page":"1220"},"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":0.9927999973297119,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9786999821662903,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9110214710235596},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7024678587913513},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6629560589790344},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4660796821117401},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44302263855934143},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38702723383903503}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9110214710235596},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7024678587913513},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6629560589790344},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4660796821117401},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44302263855934143},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38702723383903503}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icct52962.2021.9657977","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icct52962.2021.9657977","pdf_url":null,"source":{"id":"https://openalex.org/S4363607878","display_name":"2021 IEEE 21st International Conference on Communication Technology (ICCT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 21st International Conference on Communication Technology (ICCT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.550000011920929,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1120411254","https://openalex.org/W2029316659","https://openalex.org/W2072225380","https://openalex.org/W2087263574","https://openalex.org/W2132648706","https://openalex.org/W2162698522","https://openalex.org/W2321716099","https://openalex.org/W2341545927","https://openalex.org/W2547686802","https://openalex.org/W2765833609","https://openalex.org/W2774265021","https://openalex.org/W2789040646","https://openalex.org/W2991616716","https://openalex.org/W2992097261","https://openalex.org/W3013082057","https://openalex.org/W6704443453"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W2058170566","https://openalex.org/W4306674287","https://openalex.org/W2772917594","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747"],"abstract_inverted_index":{"Hyperspectral":[0,16],"remote":[1],"sensing":[2],"is":[3,129],"a":[4],"popular":[5],"research":[6],"area":[7],"that":[8],"has":[9],"gained":[10],"substantial":[11],"interest":[12],"in":[13,20],"recent":[14],"years.":[15],"images":[17,51],"are":[18,101],"characterized":[19],"hundreds":[21],"of":[22,42,49],"spectral":[23,27],"bands":[24],"with":[25,124,157],"high":[26],"resolution":[28],"and":[29,84,97,180],"this":[30,109],"immense":[31],"information":[32],"reflects":[33],"many":[34],"features.":[35],"This":[36],"paper":[37,110],"focuses":[38],"mainly":[39],"on":[40,131],"range":[41],"Machine":[43,78],"Learning":[44],"(ML)":[45],"algorithms":[46],"for":[47,103,114,137,152],"classification":[48],"hyperspectral":[50],"using":[52],"Principal":[53,116],"Component":[54],"Analysis":[55,74],"(PCA)":[56],"as":[57,65],"dimension":[58],"reduction":[59],"technique.":[60],"Popular":[61],"ML":[62],"classifiers":[63,126],"such":[64],"Random":[66],"Forest":[67],"(RF),":[68],"Logistic":[69],"Regression":[70,85],"(LR),":[71],"Linear":[72],"Discriminant":[73],"(LDA),":[75],"Support":[76],"Vector":[77],"(SVM),":[79],"K-Nearest":[80],"Neighbor":[81],"(KNN),":[82],"Classification":[83],"Trees":[86],"(CART),":[87],"Naive":[88],"Bayes":[89],"(NB),":[90],"Stochastic":[91],"Gradient":[92],"Descent":[93],"(SGD)":[94],"Classifier,":[95],"XGBoost":[96,144],"Multilayer":[98],"Perception":[99],"(MLP)":[100],"applied":[102],"8":[104],"publicly":[105],"available":[106],"datasets.":[107,141],"In":[108,122],"model":[111,120,164,191],"performance":[112],"evaluated":[113],"different":[115],"Components(PC)":[117],"considering":[118],"total":[119,190],"accuracy.":[121],"comparison":[123],"other":[125],"MLP":[127],"which":[128],"based":[130],"Neural":[132],"Network":[133],"(NN)":[134],"performed":[135],"well":[136],"all":[138],"the":[139,153],"selected":[140],"However":[142],"RF,":[143],"&":[145],"KNN":[146],"also":[147],"shows":[148],"promising":[149],"performance.":[150],"Evaluation":[151],"first":[154],"15":[155],"PC":[156],"MLP,":[158],"Pavia":[159,170],"Centre(PaviaC)":[160],"dataset":[161],"achieved":[162,182],"0.99":[163],"accuracy":[165,192],"whereas":[166],"Salinas":[167],"Corrected(SalinasC),":[168],"Salinas,":[169],"University(PaviaU),":[171],"Kennedy":[172],"Space":[173],"Center(KSC),":[174],"Indian":[175,178],"Pines":[176],"Corrected(IPC),":[177],"Pines(IP)":[179],"Bostwana":[181],"0.96,":[183,185],"0.94,":[184],"0.95,":[186],"0.83,":[187],"0.79,":[188],"0.96":[189],"respectively.":[193]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}