{"id":"https://openalex.org/W2773607448","doi":"https://doi.org/10.1109/igarss.2017.8127793","title":"Iterative clustering based active learning for hyperspectral image classification","display_name":"Iterative clustering based active learning for hyperspectral image classification","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2773607448","doi":"https://doi.org/10.1109/igarss.2017.8127793","mag":"2773607448"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2017.8127793","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8127793","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","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/A5102716602","display_name":"Ting Lu","orcid":"https://orcid.org/0000-0001-8415-0185"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ting Lu","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067097659","display_name":"Shutao Li","orcid":"https://orcid.org/0000-0002-0585-9848"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shutao Li","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035508615","display_name":"J\u00f3n Atli Benediktsson","orcid":"https://orcid.org/0000-0003-0621-9647"},"institutions":[{"id":"https://openalex.org/I165368041","display_name":"University of Iceland","ror":"https://ror.org/01db6h964","country_code":"IS","type":"education","lineage":["https://openalex.org/I165368041"]}],"countries":["IS"],"is_corresponding":false,"raw_author_name":"Jon Atli Benediktsson","raw_affiliation_strings":["Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland"],"affiliations":[{"raw_affiliation_string":"Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland","institution_ids":["https://openalex.org/I165368041"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102716602"],"corresponding_institution_ids":["https://openalex.org/I16609230"],"apc_list":null,"apc_paid":null,"fwci":0.2354,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.63357828,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"3664","last_page":"3667"},"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/T12676","display_name":"Machine Learning and ELM","score":0.975600004196167,"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/T10207","display_name":"Advanced biosensing and bioanalysis techniques","score":0.9631999731063843,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7287254929542542},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7066595554351807},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6986038088798523},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6917278170585632},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6386488676071167},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.5956687331199646},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5819410085678101},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.47582271695137024},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4721890687942505},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4432010054588318},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.43870487809181213},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.42154112458229065},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4212660491466522},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3877059519290924},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33808866143226624},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3008005619049072},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.17668309807777405},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1294419765472412}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7287254929542542},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7066595554351807},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6986038088798523},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6917278170585632},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6386488676071167},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.5956687331199646},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5819410085678101},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.47582271695137024},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4721890687942505},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4432010054588318},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.43870487809181213},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.42154112458229065},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4212660491466522},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3877059519290924},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33808866143226624},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3008005619049072},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.17668309807777405},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1294419765472412},{"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},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2017.8127793","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8127793","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1651266332","https://openalex.org/W1972524915","https://openalex.org/W2101365302","https://openalex.org/W2107131609","https://openalex.org/W2136251662","https://openalex.org/W2155342973","https://openalex.org/W2315347323","https://openalex.org/W2518831014"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4312814274","https://openalex.org/W2072166414","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2147064750"],"abstract_inverted_index":{"In":[0],"this":[1,131],"paper,":[2],"a":[3,74,170],"novel":[4],"iterative":[5,53],"clustering":[6,54],"based":[7,55,91],"active":[8],"learning":[9,24],"(ICAL)":[10],"method":[11,162],"for":[12,34],"hyperspectral":[13],"image":[14],"classification":[15,46,92,153,166],"is":[16,26,59,85,122],"proposed.":[17],"On":[18,48],"the":[19,22,29,49,64,81,89,99,102,115,119,125,135,150,159],"one":[20,116],"hand,":[21,51],"extreme":[23],"machine":[25],"combined":[27],"with":[28,118,169],"Markov":[30],"random":[31],"field":[32],"(ELM-MRF)":[33],"label":[35],"assignment,":[36],"to":[37,44,61,80,108,124,149],"exploit":[38],"both":[39],"spectral":[40,109],"and":[41,137],"spatial":[42],"information":[43],"boost":[45],"result.":[47],"other":[50],"an":[52],"sample":[56,68,117,128,132],"selection":[57,133],"strategy":[58,71],"introduced":[60],"optimally":[62],"choose":[63],"most":[65],"informative":[66],"training":[67,127,140],"set.":[69,129],"This":[70],"first":[72],"selects":[73],"candidate":[75,103],"set":[76,104],"of":[77,139,152,173],"samples,":[78],"according":[79,107],"differential":[82],"map":[83],"that":[84,158],"obtained":[86],"by":[87],"comparing":[88],"ELM-MRF":[90],"results":[93,156,167],"in":[94,101],"adjacent":[95],"iterations.":[96],"Then,":[97],"all":[98],"pixels":[100],"are":[105],"clustered":[106],"characteristics.":[110],"Finally,":[111],"from":[112],"each":[113],"cluster,":[114],"highest":[120],"uncertainty":[121,138],"added":[123],"new":[126],"By":[130],"strategy,":[134],"diversity":[136],"samples":[141],"can":[142,146,163],"be":[143],"maximized,":[144],"which":[145],"further":[147],"contribute":[148],"improvement":[151],"performance.":[154],"Experimental":[155],"show":[157],"proposed":[160],"ICAL":[161],"achieve":[164],"competitive":[165],"even":[168],"limited":[171],"number":[172],"labeled":[174],"samples.":[175]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}