{"id":"https://openalex.org/W2044822643","doi":"https://doi.org/10.1109/igarss.2015.7325785","title":"Semi-supervised co-training and active learning framework for hyperspectral image classification","display_name":"Semi-supervised co-training and active learning framework for hyperspectral image classification","publication_year":2015,"publication_date":"2015-07-01","ids":{"openalex":"https://openalex.org/W2044822643","doi":"https://doi.org/10.1109/igarss.2015.7325785","mag":"2044822643"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2015.7325785","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2015.7325785","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5019337252","display_name":"Sathishkumar Samiappan","orcid":"https://orcid.org/0000-0002-8443-883X"},"institutions":[{"id":"https://openalex.org/I99041443","display_name":"Mississippi State University","ror":"https://ror.org/0432jq872","country_code":"US","type":"education","lineage":["https://openalex.org/I4210141039","https://openalex.org/I99041443"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sathishkumar Samiappan","raw_affiliation_strings":["Geosystems Research Institute, Mississippi State University, MS","Geosystems Research Institute, Mississippi State University, Mississippi State, MS, 39762"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Geosystems Research Institute, Mississippi State University, MS","institution_ids":["https://openalex.org/I99041443"]},{"raw_affiliation_string":"Geosystems Research Institute, Mississippi State University, Mississippi State, MS, 39762","institution_ids":["https://openalex.org/I99041443"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056355204","display_name":"Robert Moorhead","orcid":"https://orcid.org/0000-0002-4642-7873"},"institutions":[{"id":"https://openalex.org/I99041443","display_name":"Mississippi State University","ror":"https://ror.org/0432jq872","country_code":"US","type":"education","lineage":["https://openalex.org/I4210141039","https://openalex.org/I99041443"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robert J. Moorhead","raw_affiliation_strings":["Geosystems Research Institute, Mississippi State University, MS","Geosystems Research Institute, Mississippi State University, Mississippi State, MS, 39762"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Geosystems Research Institute, Mississippi State University, MS","institution_ids":["https://openalex.org/I99041443"]},{"raw_affiliation_string":"Geosystems Research Institute, Mississippi State University, Mississippi State, MS, 39762","institution_ids":["https://openalex.org/I99041443"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99041443"],"apc_list":null,"apc_paid":null,"fwci":4.8408,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.96757435,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"401","last_page":"404"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9994000196456909,"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":0.9994000196456909,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9585999846458435,"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"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9480000138282776,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9425865411758423},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6866530179977417},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6678529381752014},{"id":"https://openalex.org/keywords/co-training","display_name":"Co-training","score":0.6428472995758057},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.604315459728241},{"id":"https://openalex.org/keywords/imaging-spectrometer","display_name":"Imaging spectrometer","score":0.5399127006530762},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.539171576499939},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5055451393127441},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.49586424231529236},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.47255560755729675},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4594370722770691},{"id":"https://openalex.org/keywords/full-spectral-imaging","display_name":"Full spectral imaging","score":0.45163780450820923},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.43769675493240356},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4355211853981018},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4097101390361786},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36485999822616577},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.2994596064090729},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2586830258369446},{"id":"https://openalex.org/keywords/spectrometer","display_name":"Spectrometer","score":0.12788870930671692}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9425865411758423},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6866530179977417},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6678529381752014},{"id":"https://openalex.org/C2776959682","wikidata":"https://www.wikidata.org/wiki/Q17005296","display_name":"Co-training","level":3,"score":0.6428472995758057},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.604315459728241},{"id":"https://openalex.org/C183852935","wikidata":"https://www.wikidata.org/wiki/Q6002848","display_name":"Imaging spectrometer","level":3,"score":0.5399127006530762},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.539171576499939},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5055451393127441},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.49586424231529236},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.47255560755729675},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4594370722770691},{"id":"https://openalex.org/C78660771","wikidata":"https://www.wikidata.org/wiki/Q5508206","display_name":"Full spectral imaging","level":3,"score":0.45163780450820923},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.43769675493240356},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4355211853981018},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4097101390361786},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36485999822616577},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.2994596064090729},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2586830258369446},{"id":"https://openalex.org/C33390570","wikidata":"https://www.wikidata.org/wiki/Q188463","display_name":"Spectrometer","level":2,"score":0.12788870930671692},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2015.7325785","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2015.7325785","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 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":12,"referenced_works":["https://openalex.org/W2030476695","https://openalex.org/W2048679005","https://openalex.org/W2097238823","https://openalex.org/W2106092565","https://openalex.org/W2107131609","https://openalex.org/W2128518360","https://openalex.org/W2132532785","https://openalex.org/W2134663338","https://openalex.org/W2136504847","https://openalex.org/W2142012908","https://openalex.org/W2153409933","https://openalex.org/W6680140577"],"related_works":["https://openalex.org/W3143887424","https://openalex.org/W2394476741","https://openalex.org/W2332153559","https://openalex.org/W2036976283","https://openalex.org/W2761280200","https://openalex.org/W2106849321","https://openalex.org/W2047406830","https://openalex.org/W2766336871","https://openalex.org/W2733444092","https://openalex.org/W2171464537"],"abstract_inverted_index":{"Hyperspectral":[0],"imaging":[1],"enables":[2],"detailed":[3],"ground":[4,51],"cover":[5],"classification":[6,24],"with":[7,136],"hundreds":[8],"of":[9,25,35,48,68,83,120,161,172],"spectral":[10,16],"bands":[11],"at":[12],"each":[13],"pixel.":[14],"Rich":[15],"information":[17],"can":[18],"be":[19],"a":[20,26,30,45,80,95,103,127],"drawback":[21],"since":[22],"supervised":[23],"hyperspectral":[27,140,173],"image":[28,141,174],"requires":[29,44],"balance":[31,43],"between":[32],"the":[33,66,108,121,144,159,170],"number":[34,47],"training":[36,49,74,113],"samples":[37,75,86,114],"and":[38,59,147],"its":[39],"dimension.":[40],"Achieving":[41],"this":[42,91,162],"large":[46],"or":[50],"truth":[52],"samples,":[53],"which":[54,98,107],"is":[55,124],"generally":[56],"difficult,":[57],"expensive":[58],"time-consuming.":[60],"This":[61],"led":[62],"researchers":[63],"to":[64,102],"explore":[65],"use":[67,160],"semi-supervised":[69,96],"learning":[70,101],"techniques":[71],"where":[72],"new":[73,112],"(unlabeled)":[76],"are":[77],"obtained":[78],"from":[79,115],"small":[81],"set":[82],"available":[84],"labeled":[85],"without":[87],"significant":[88],"effort.":[89],"In":[90],"paper,":[92],"we":[93],"propose":[94],"approach":[97,123,165],"adapts":[99],"active":[100],"co-training":[104,163],"framework":[105],"in":[106,169],"algorithm":[109],"automatically":[110],"selects":[111],"abundant":[116],"unlabeled":[117],"pixels.":[118],"Efficacy":[119],"proposed":[122],"validated":[125],"using":[126],"probabilistic":[128],"support":[129],"vector":[130],"machine":[131],"classifier.":[132],"Our":[133],"experimental":[134],"results":[135],"an":[137],"Indian":[138],"Pines":[139],"collected":[142],"by":[143],"National":[145],"Aeronautics":[146],"Space":[148],"Administration":[149],"Jet":[150],"Propulsion":[151],"Laboratory's":[152],"Airborne":[153],"Visible-Infrared":[154],"Imaging":[155],"Spectrometer":[156],"indicate":[157],"that":[158],"based":[164],"represents":[166],"promising":[167],"strategy":[168],"context":[171],"classification.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
