{"id":"https://openalex.org/W3207834825","doi":"https://doi.org/10.1109/igarss47720.2021.9554830","title":"Boosting CNN for Hyperspectral Image Classification","display_name":"Boosting CNN for Hyperspectral Image Classification","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3207834825","doi":"https://doi.org/10.1109/igarss47720.2021.9554830","mag":"3207834825"},"language":"en","primary_location":{"id":"doi:10.1109/igarss47720.2021.9554830","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554830","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 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/A5100425881","display_name":"Haoyu Zhang","orcid":"https://orcid.org/0000-0001-9260-0270"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoyu Zhang","raw_affiliation_strings":["Electronic Information Engineering, Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electronic Information Engineering, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027034650","display_name":"Yushi Chen","orcid":"https://orcid.org/0000-0003-2421-0996"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yushi Chen","raw_affiliation_strings":["Electronic Information Engineering, Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electronic Information Engineering, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039815946","display_name":"Xin He","orcid":"https://orcid.org/0000-0003-0455-4230"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin He","raw_affiliation_strings":["Electronic Information Engineering, Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electronic Information Engineering, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102986089","display_name":"Xingliang Shen","orcid":"https://orcid.org/0000-0003-1248-0470"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xingliang Shen","raw_affiliation_strings":["Tianjin Navigation Instruments Research Institute, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin Navigation Instruments Research Institute, Tianjin, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3673","last_page":"3676"},"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":0.9854000210762024,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9502000212669373,"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/boosting","display_name":"Boosting (machine learning)","score":0.8993316888809204},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8066944479942322},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7566637992858887},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7555795907974243},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7134850025177002},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7123826742172241},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.6086642742156982},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5372801423072815},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43020838499069214},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.41916748881340027},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41485440731048584},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2529967129230499}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.8993316888809204},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8066944479942322},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7566637992858887},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7555795907974243},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7134850025177002},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7123826742172241},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.6086642742156982},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5372801423072815},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43020838499069214},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.41916748881340027},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41485440731048584},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2529967129230499}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss47720.2021.9554830","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554830","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 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":10,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W1975846642","https://openalex.org/W2000563363","https://openalex.org/W2085529604","https://openalex.org/W2087263574","https://openalex.org/W2114819256","https://openalex.org/W2155806188","https://openalex.org/W2167397429","https://openalex.org/W2169500530","https://openalex.org/W2603834682"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2905156999","https://openalex.org/W4229460275","https://openalex.org/W4296079469","https://openalex.org/W1987518466","https://openalex.org/W3023033471","https://openalex.org/W3135046080","https://openalex.org/W2809161969","https://openalex.org/W2565656575"],"abstract_inverted_index":{"In":[0,114],"recent":[1],"years,":[2],"deep":[3,49,56],"convolutional":[4],"neural":[5],"networks":[6],"(CNNs)":[7],"have":[8,70],"been":[9],"widely":[10],"used":[11],"for":[12,40],"hyperspectral":[13,143],"image":[14],"(HSI)":[15],"classification.":[16],"Besides,":[17],"ensemble":[18,52],"learning":[19],"is":[20,38,84,128],"a":[21,33,68],"useful":[22],"way":[23],"to":[24,60,116,130,160],"enhance":[25],"the":[26,46,74,96,132,152],"classification":[27,82,94,107,157],"performance.":[28],"Therefore,":[29],"in":[30,67,73,100,121],"this":[31,111],"study,":[32],"new":[34],"method":[35,154],"titled":[36],"Boosting-CNN":[37],"proposed":[39,129,153],"HSI":[41,93,122],"classification,":[42,123],"which":[43],"fully":[44],"explored":[45],"advantages":[47],"of":[48,89,98,134],"CNN":[50,69,76],"and":[51,147],"learning.":[53],"Specifically,":[54],"several":[55,90],"CNNs":[57],"are":[58,65],"well-designed":[59],"classify":[61],"HSI.":[62],"The":[63,80],"samples":[64,99,120],"misclassified":[66],"more":[71],"weighs":[72],"following":[75],"through":[77],"adaptive":[78],"boosting.":[79],"final":[81],"result":[83],"obtained":[85],"by":[86],"weighted":[87],"voting":[88],"CNNs.":[91],"For":[92],"issue,":[95],"number":[97],"different":[101],"classes":[102],"varies":[103],"greatly,":[104],"however,":[105],"traditional":[106],"methods":[108],"cannot":[109],"handle":[110],"issue":[112],"well.":[113],"order":[115],"address":[117],"imbalance":[118,135],"training":[119,136],"soft":[124],"class":[125],"balanced":[126],"loss":[127],"mitigate":[131],"influence":[133],"samples.":[137],"Experimental":[138],"results":[139],"on":[140],"two":[141],"popular":[142],"datasets":[144],"(i.e.,":[145],"Salinas":[146],"Pavia":[148],"University)":[149],"show":[150],"that":[151],"obtain":[155],"better":[156],"accuracy":[158],"compared":[159],"comparison":[161],"methods.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}