{"id":"https://openalex.org/W3127327694","doi":"https://doi.org/10.1109/tgrs.2021.3053204","title":"Iterative Training Sampling Coupled With Active Learning for Semisupervised Spectral\u2013Spatial Hyperspectral Image Classification","display_name":"Iterative Training Sampling Coupled With Active Learning for Semisupervised Spectral\u2013Spatial Hyperspectral Image Classification","publication_year":2021,"publication_date":"2021-02-05","ids":{"openalex":"https://openalex.org/W3127327694","doi":"https://doi.org/10.1109/tgrs.2021.3053204","mag":"3127327694"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2021.3053204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2021.3053204","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","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/A5066795807","display_name":"Kenneth Yeonkong","orcid":"https://orcid.org/0000-0003-3636-5311"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kenneth Yeonkong Ma","raw_affiliation_strings":["Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore County, Baltimore, MD, USA"],"raw_orcid":"https://orcid.org/0000-0003-3636-5311","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]},{"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/I177918364","display_name":"Providence University","ror":"https://ror.org/03fcpsq87","country_code":"TW","type":"education","lineage":["https://openalex.org/I177918364"]},{"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"]},{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["CN","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 County, Baltimore, MD, USA","Center of Hyperspectral Imaging in Remote Sensing, Information and Technology College, Dalian Maritime University, Dalian, China","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 County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]},{"raw_affiliation_string":"Center of Hyperspectral Imaging in Remote Sensing, Information and Technology College, Dalian Maritime University, Dalian, China","institution_ids":["https://openalex.org/I43313876"]},{"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":2.9898,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.91664133,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"59","issue":"10","first_page":"8672","last_page":"8692"},"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9747999906539917,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9708999991416931,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7973507642745972},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6628158688545227},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6170739531517029},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.587831974029541},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5781316161155701},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5648949146270752},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5169014930725098},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.4812205135822296},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4810933768749237},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4409598410129547},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.42480039596557617},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37023186683654785},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.34961187839508057},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27186423540115356},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.21552741527557373},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.1564115583896637}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7973507642745972},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6628158688545227},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6170739531517029},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.587831974029541},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5781316161155701},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5648949146270752},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5169014930725098},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.4812205135822296},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4810933768749237},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4409598410129547},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.42480039596557617},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37023186683654785},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.34961187839508057},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27186423540115356},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21552741527557373},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.1564115583896637},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2021.3053204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2021.3053204","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1447177257","display_name":null,"funder_award_id":"3132019341","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1482289351","https://openalex.org/W1522547150","https://openalex.org/W1554663460","https://openalex.org/W1664950380","https://openalex.org/W2001298023","https://openalex.org/W2014327494","https://openalex.org/W2016860790","https://openalex.org/W2030476695","https://openalex.org/W2031510368","https://openalex.org/W2062822804","https://openalex.org/W2092869901","https://openalex.org/W2101365302","https://openalex.org/W2103568877","https://openalex.org/W2104269704","https://openalex.org/W2113464037","https://openalex.org/W2114819256","https://openalex.org/W2115305054","https://openalex.org/W2125188192","https://openalex.org/W2131864940","https://openalex.org/W2136251662","https://openalex.org/W2152057649","https://openalex.org/W2164330327","https://openalex.org/W2164437025","https://openalex.org/W2166923144","https://openalex.org/W2516506474","https://openalex.org/W2548695521","https://openalex.org/W2606507269","https://openalex.org/W2743091961","https://openalex.org/W2754659306","https://openalex.org/W2782051899","https://openalex.org/W2809113079","https://openalex.org/W2899937451","https://openalex.org/W2907064575","https://openalex.org/W2944896556","https://openalex.org/W2971027447","https://openalex.org/W3005983949","https://openalex.org/W3040782625","https://openalex.org/W3041591456","https://openalex.org/W3105243393","https://openalex.org/W4245539475","https://openalex.org/W4250800088","https://openalex.org/W4388297464","https://openalex.org/W6630424276"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2070598848","https://openalex.org/W3034375524","https://openalex.org/W2027399350","https://openalex.org/W2060875994","https://openalex.org/W2044184146","https://openalex.org/W2019190440","https://openalex.org/W3034864990","https://openalex.org/W4230131218"],"abstract_inverted_index":{"Training":[0],"sample":[1,33],"selection":[2,34],"is":[3,40,60,124,162],"a":[4,15,88],"great":[5],"challenge":[6],"for":[7,25,31],"hyperspectral":[8],"image":[9],"classification":[10,73,93,112,138,174,179],"(HSIC),":[11],"specifically":[12],"when":[13],"only":[14,171],"very":[16],"limited":[17],"number":[18],"of":[19,36,122,128],"labeled":[20,46],"data":[21,52,67,132,145],"samples":[22,48,53,146,152],"are":[23,35],"available":[24],"training.":[26],"Two":[27],"recently":[28],"developed":[29],"concepts":[30],"training":[32,47,56,62,77,151],"particular":[37],"interest.":[38],"One":[39],"active":[41],"learning":[42],"(AL),":[43],"which":[44,65,108],"augments":[45],"by":[49,69,100,134,154],"including":[50,70],"unlabeled":[51,144],"as":[54,106],"new":[55],"samples.":[57,78],"The":[58,119],"other":[59],"iterative":[61],"sampling":[63],"(ITS),":[64],"expands":[66],"cubes":[68,133],"additional":[71,136],"spatial":[72,137],"information":[74,139],"into":[75,141],"the":[76,142,149],"This":[79],"article":[80],"combines":[81],"AL":[82,101,115,168],"and":[83,167],"ITS":[84,98,117,129,166],"simultaneously":[85],"to":[86,95,105,125,130,147,169],"derive":[87],"joint":[89],"ITS\u2013AL":[90],"spectral\u2013spatial":[91,102],"(SS)":[92],"approach,":[94],"be":[96],"called":[97],"augmentation":[99],"classification,":[103],"referred":[104],"ITSA-AL-SS,":[107],"can":[109],"improve":[110,173],"SS":[111],"using":[113],"either":[114],"or":[116],"alone.":[118],"novel":[120],"idea":[121],"ITSA-AL-SS":[123,161],"take":[126],"advantage":[127],"expand":[131],"incorporating":[135],"iteratively":[140],"AL-augmented":[143],"update":[148],"current":[150],"iteration":[153,155],"in":[156],"one-shot":[157],"operation.":[158],"As":[159],"expected,":[160],"benefited":[163],"from":[164],"both":[165],"not":[170],"further":[172],"accuracy":[175],"but":[176],"also":[177],"reduce":[178],"inconsistency.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}