{"id":"https://openalex.org/W3131722669","doi":"https://doi.org/10.1109/igarss39084.2020.9323629","title":"Spectral-Spatial Feature Extraction based CNN for Hyperspectral Image Classification","display_name":"Spectral-Spatial Feature Extraction based CNN for Hyperspectral Image Classification","publication_year":2020,"publication_date":"2020-09-26","ids":{"openalex":"https://openalex.org/W3131722669","doi":"https://doi.org/10.1109/igarss39084.2020.9323629","mag":"3131722669"},"language":"en","primary_location":{"id":"doi:10.1109/igarss39084.2020.9323629","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323629","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","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/A5008256662","display_name":"Yinghui Quan","orcid":"https://orcid.org/0000-0001-6541-9441"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinghui Quan","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi'an, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi'an, P. R. China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028522401","display_name":"Shuxian Dong","orcid":"https://orcid.org/0000-0001-6390-5585"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuxian Dong","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi'an, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi'an, P. R. China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068336830","display_name":"Wei Feng","orcid":"https://orcid.org/0000-0003-1907-2664"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Feng","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi'an, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi'an, P. R. China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044570096","display_name":"Gabriel Dauphin","orcid":"https://orcid.org/0000-0002-0677-6702"},"institutions":[{"id":"https://openalex.org/I4210091279","display_name":"Universit\u00e9 Sorbonne Paris Nord","ror":"https://ror.org/0199hds37","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210091279"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Gabriel Dauphin","raw_affiliation_strings":["Laboratory of Information Processing and Transmission, L2TI, Institut Galil\u00e9e, University Paris XIII, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laboratory of Information Processing and Transmission, L2TI, Institut Galil\u00e9e, University Paris XIII, France","institution_ids":["https://openalex.org/I4210091279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022141624","display_name":"Guoping Zhao","orcid":"https://orcid.org/0000-0002-7621-6620"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guoping Zhao","raw_affiliation_strings":["Key Laboratory of State Forestry Administration on Soil Land Water Conservation & Ecological Restoration of the Loess Plateau, Shaan Xi Academy of Forestry, Xi'an, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of State Forestry Administration on Soil Land Water Conservation & Ecological Restoration of the Loess Plateau, Shaan Xi Academy of Forestry, Xi'an, P. R. China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100424542","display_name":"Yong Wang","orcid":"https://orcid.org/0000-0003-3366-3137"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Wang","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi'an, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi'an, P. R. China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021408538","display_name":"Mengdao Xing","orcid":"https://orcid.org/0000-0002-4084-0915"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengdao Xing","raw_affiliation_strings":["Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an, P. R. China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"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":"2015","issue":null,"first_page":"485","last_page":"488"},"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":0.9962000250816345,"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/T10057","display_name":"Face and Expression Recognition","score":0.9742000102996826,"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":0.9150471687316895},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7786548733711243},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7650586366653442},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7386699914932251},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7273619771003723},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.7074868083000183},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5359673500061035},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5254506468772888},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5117549896240234},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5063740015029907},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4578818380832672},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.423542320728302},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3449188768863678}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9150471687316895},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7786548733711243},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7650586366653442},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7386699914932251},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7273619771003723},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.7074868083000183},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5359673500061035},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5254506468772888},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5117549896240234},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5063740015029907},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4578818380832672},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.423542320728302},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3449188768863678},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/igarss39084.2020.9323629","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323629","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-03916050v1","is_oa":false,"landing_page_url":"https://hal.science/hal-03916050","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Sep 2020, Waikoloa, France. pp.485-488, &#x27E8;10.1109/IGARSS39084.2020.9323629&#x27E9;","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4840223945","display_name":"\u9762\u5411\u5f39\u8f7d\u5fae\u6ce2\u524d\u89c6\u4e09\u7ef4\u6210\u50cf\u5236\u5bfc\u7684\u5f02\u6784\u53ef\u91cd\u6784\u8ba1\u7b97\u7814\u7a76","funder_award_id":"61772397","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G702456963","display_name":null,"funder_award_id":"2016YFE0200400","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320323230","display_name":"Xidian University","ror":"https://ror.org/05s92vm98"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"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":10,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W2031510368","https://openalex.org/W2154240401","https://openalex.org/W2548476632","https://openalex.org/W2548791488","https://openalex.org/W2943270518","https://openalex.org/W2946655868","https://openalex.org/W2953926847","https://openalex.org/W2957718075","https://openalex.org/W2985701536"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W2786391746","https://openalex.org/W2565656575","https://openalex.org/W4381430104","https://openalex.org/W2995102745","https://openalex.org/W4226059458","https://openalex.org/W2914559142","https://openalex.org/W1990237101"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1],"networks":[2],"(CNN)":[3],"can":[4],"automatically":[5],"learn":[6],"features":[7,45],"from":[8],"the":[9,16,23,29,83,86],"hyperspectral":[10,32,80],"image":[11],"data,":[12],"which":[13],"could":[14],"avoid":[15],"difficulty":[17],"of":[18,25,31,85],"manually":[19],"extracting":[20],"features.":[21],"However,":[22],"number":[24],"training":[26,72],"set":[27],"for":[28,40,65],"classification":[30,50,68],"images":[33,81],"is":[34,63],"always":[35],"limited,":[36],"making":[37],"it":[38],"difficult":[39],"CNN":[41,61],"to":[42],"obtain":[43],"effective":[44],"and":[46],"resulting":[47],"in":[48],"low":[49],"accuracy.":[51],"In":[52],"this":[53],"paper,":[54],"a":[55,70],"spectral-spatial":[56],"feature":[57],"(SSF)":[58],"extraction":[59],"based":[60,76],"method":[62],"proposed":[64,87],"an":[66],"accurate":[67],"with":[69],"small":[71],"set.":[73],"Experimental":[74],"results":[75],"on":[77],"two":[78],"standard":[79],"demonstrate":[82],"effectiveness":[84],"method.":[88]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2022,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
