{"id":"https://openalex.org/W2795478733","doi":"https://doi.org/10.3390/rs10040574","title":"Semi-Supervised Deep Learning Classification for Hyperspectral Image Based on Dual-Strategy Sample Selection","display_name":"Semi-Supervised Deep Learning Classification for Hyperspectral Image Based on Dual-Strategy Sample Selection","publication_year":2018,"publication_date":"2018-04-08","ids":{"openalex":"https://openalex.org/W2795478733","doi":"https://doi.org/10.3390/rs10040574","mag":"2795478733"},"language":"en","primary_location":{"id":"doi:10.3390/rs10040574","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs10040574","pdf_url":"https://www.mdpi.com/2072-4292/10/4/574/pdf?version=1525347548","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/10/4/574/pdf?version=1525347548","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038891359","display_name":"Bei Fang","orcid":"https://orcid.org/0000-0002-3461-7995"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bei Fang","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China","School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100785617","display_name":"Ying Li","orcid":"https://orcid.org/0000-0001-7370-1754"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ying Li","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China","School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018764462","display_name":"Haokui Zhang","orcid":"https://orcid.org/0000-0002-4336-5558"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haokui Zhang","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China","School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047379149","display_name":"Jonathan Cheung-Wai Chan","orcid":"https://orcid.org/0000-0002-3741-1124"},"institutions":[{"id":"https://openalex.org/I13469542","display_name":"Vrije Universiteit Brussel","ror":"https://ror.org/006e5kg04","country_code":"BE","type":"education","lineage":["https://openalex.org/I13469542"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Jonathan Chan","raw_affiliation_strings":["Department of Electronics and Informatics, Vrije Universiteit Brussel, Brussel 1050, Belgium"],"affiliations":[{"raw_affiliation_string":"Department of Electronics and Informatics, Vrije Universiteit Brussel, Brussel 1050, Belgium","institution_ids":["https://openalex.org/I13469542"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100785617"],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":7.3143,"has_fulltext":false,"cited_by_count":61,"citation_normalized_percentile":{"value":0.97282516,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"10","issue":"4","first_page":"574","last_page":"574"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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":1.0,"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":0.9882000088691711,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9725000262260437,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8010313510894775},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7948675751686096},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7642755508422852},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6556850671768188},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6384745836257935},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6078566312789917},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5954431891441345},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5114952325820923},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5037903189659119},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49184107780456543},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4478507936000824},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4209873080253601},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.42078667879104614},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.05536732077598572}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8010313510894775},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7948675751686096},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7642755508422852},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6556850671768188},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6384745836257935},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6078566312789917},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5954431891441345},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5114952325820923},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5037903189659119},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49184107780456543},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4478507936000824},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4209873080253601},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.42078667879104614},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.05536732077598572},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"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/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs10040574","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs10040574","pdf_url":"https://www.mdpi.com/2072-4292/10/4/574/pdf?version=1525347548","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:fd5bd8cd7e1f41c88b21283e43e1f551","is_oa":true,"landing_page_url":"https://doaj.org/article/fd5bd8cd7e1f41c88b21283e43e1f551","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 10, Iss 4, p 574 (2018)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/10/4/574/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs10040574","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 10; Issue 4; Pages: 574","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs10040574","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs10040574","pdf_url":"https://www.mdpi.com/2072-4292/10/4/574/pdf?version=1525347548","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2795478733.pdf","grobid_xml":"https://content.openalex.org/works/W2795478733.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W46659105","https://openalex.org/W1836465849","https://openalex.org/W1950365613","https://openalex.org/W2029316659","https://openalex.org/W2048679005","https://openalex.org/W2063837453","https://openalex.org/W2069231830","https://openalex.org/W2076434944","https://openalex.org/W2087263574","https://openalex.org/W2087874437","https://openalex.org/W2090424610","https://openalex.org/W2108598243","https://openalex.org/W2147809505","https://openalex.org/W2163605009","https://openalex.org/W2193145675","https://openalex.org/W2194775991","https://openalex.org/W2276858186","https://openalex.org/W2314785379","https://openalex.org/W2345128667","https://openalex.org/W2500751094","https://openalex.org/W2520588193","https://openalex.org/W2522698497","https://openalex.org/W2528211483","https://openalex.org/W2531736579","https://openalex.org/W2572303978","https://openalex.org/W2577238056","https://openalex.org/W2614326984","https://openalex.org/W2618530766","https://openalex.org/W2745252301","https://openalex.org/W2919115771","https://openalex.org/W2963446712","https://openalex.org/W3106250896","https://openalex.org/W6602002561","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2404757046","https://openalex.org/W2044184146","https://openalex.org/W2070598848","https://openalex.org/W4313014865","https://openalex.org/W2990636717"],"abstract_inverted_index":{"This":[0],"paper":[1],"studies":[2],"the":[3,12,33,66,102,110,114,124,175],"classification":[4,171],"problem":[5],"of":[6,15,35,83,109,113,138,149],"hyperspectral":[7,36],"image":[8],"(HSI).":[9],"Inspired":[10],"by":[11,77,105],"great":[13],"success":[14],"deep":[16,27,39,61],"neural":[17,155],"networks":[18,68],"in":[19,119],"Artificial":[20],"Intelligence":[21],"(AI),":[22],"researchers":[23],"have":[24],"proposed":[25],"different":[26],"learning":[28,40,62],"based":[29,41,64],"algorithms":[30,42],"to":[31,49,99],"improve":[32],"performance":[34,145],"classification.":[37,121],"However,":[38],"always":[43],"require":[44],"a":[45,59,87,135],"large-scale":[46],"annotated":[47],"dataset":[48,127,131],"provide":[50],"sufficient":[51],"training.":[52],"To":[53],"address":[54],"this":[55],"problem,":[56],"we":[57],"propose":[58],"semi-supervised":[60,182],"framework":[63,85],"on":[65,123,165],"residual":[67],"(ResNets),":[69],"which":[70,94],"use":[71,108],"very":[72],"limited":[73],"labeled":[74],"data":[75,104],"supplemented":[76],"abundant":[78],"unlabeled":[79,103],"data.":[80],"The":[81],"core":[82],"our":[84,141],"is":[86],"novel":[88],"dual-strategy":[89],"sample":[90],"selection":[91],"co-training":[92],"algorithm,":[93],"can":[95],"successfully":[96],"guide":[97],"ResNets":[98],"learn":[100],"from":[101,177],"making":[106],"full":[107],"complementary":[111],"cues":[112],"spectral":[115],"and":[116,128,181],"spatial":[117],"features":[118],"HSI":[120,126,130,170],"Experiments":[122],"benchmark":[125],"real":[129],"demonstrate":[132],"that,":[133],"with":[134,146,152,158,174],"small":[136],"number":[137],"training":[139,161],"data,":[140],"approach":[142],"achieves":[143],"competitive":[144],"maximum":[147],"improvement":[148],"41%":[150],"(compare":[151],"traditional":[153],"convolutional":[154],"network":[156],"(CNN)":[157],"5":[159],"initial":[160],"samples":[162],"per":[163],"class":[164],"Indian":[166],"Pines":[167],"dataset)":[168],"for":[169],"as":[172],"compared":[173],"results":[176],"those":[178],"state-of-the-art":[179],"supervised":[180],"methods.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":14},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":1}],"updated_date":"2026-04-06T07:47:59.780226","created_date":"2025-10-10T00:00:00"}