{"id":"https://openalex.org/W2965615220","doi":"https://doi.org/10.3390/rs11151794","title":"Alternately Updated Spectral\u2013Spatial Convolution Network for the Classification of Hyperspectral Images","display_name":"Alternately Updated Spectral\u2013Spatial Convolution Network for the Classification of Hyperspectral Images","publication_year":2019,"publication_date":"2019-07-31","ids":{"openalex":"https://openalex.org/W2965615220","doi":"https://doi.org/10.3390/rs11151794","mag":"2965615220"},"language":"en","primary_location":{"id":"doi:10.3390/rs11151794","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11151794","pdf_url":"https://www.mdpi.com/2072-4292/11/15/1794/pdf?version=1565158437","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/2072-4292/11/15/1794/pdf?version=1565158437","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001514481","display_name":"Wenju Wang","orcid":"https://orcid.org/0000-0002-8549-4710"},"institutions":[{"id":"https://openalex.org/I148128674","display_name":"University of Shanghai for Science and Technology","ror":"https://ror.org/00ay9v204","country_code":"CN","type":"education","lineage":["https://openalex.org/I148128674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenju Wang","raw_affiliation_strings":["College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai 200093, China"],"affiliations":[{"raw_affiliation_string":"College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai 200093, China","institution_ids":["https://openalex.org/I148128674"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039546706","display_name":"Shuguang Dou","orcid":"https://orcid.org/0000-0003-3231-8817"},"institutions":[{"id":"https://openalex.org/I148128674","display_name":"University of Shanghai for Science and Technology","ror":"https://ror.org/00ay9v204","country_code":"CN","type":"education","lineage":["https://openalex.org/I148128674"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shuguang Dou","raw_affiliation_strings":["College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai 200093, China"],"affiliations":[{"raw_affiliation_string":"College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai 200093, China","institution_ids":["https://openalex.org/I148128674"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100350760","display_name":"Sen Wang","orcid":"https://orcid.org/0000-0003-1537-8834"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sen Wang","raw_affiliation_strings":["Institute of Information Technology, Shanghai Baosight Software Co., Ltd., Shanghai 200940, China"],"affiliations":[{"raw_affiliation_string":"Institute of Information Technology, Shanghai Baosight Software Co., Ltd., Shanghai 200940, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5039546706"],"corresponding_institution_ids":["https://openalex.org/I148128674"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":2.8568,"has_fulltext":true,"cited_by_count":25,"citation_normalized_percentile":{"value":0.9155653,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"11","issue":"15","first_page":"1794","last_page":"1794"},"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":0.9961000084877014,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9768999814987183,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7369049787521362},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7317792773246765},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7096890211105347},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6947593092918396},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.656855583190918},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6552020311355591},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6093865633010864},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4932405352592468},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3241969645023346},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.23825445771217346},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.0692296028137207}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7369049787521362},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7317792773246765},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7096890211105347},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6947593092918396},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.656855583190918},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6552020311355591},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6093865633010864},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4932405352592468},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3241969645023346},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.23825445771217346},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0692296028137207}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs11151794","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11151794","pdf_url":"https://www.mdpi.com/2072-4292/11/15/1794/pdf?version=1565158437","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:d05f4d1ecbdf4a37a26d5fa29d7c30c4","is_oa":true,"landing_page_url":"https://doaj.org/article/d05f4d1ecbdf4a37a26d5fa29d7c30c4","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 11, Iss 15, p 1794 (2019)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/11/15/1794/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs11151794","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","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs11151794","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11151794","pdf_url":"https://www.mdpi.com/2072-4292/11/15/1794/pdf?version=1565158437","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":[{"display_name":"Reduced inequalities","score":0.5600000023841858,"id":"https://metadata.un.org/sdg/10"},{"display_name":"Peace, Justice and strong institutions","score":0.4000000059604645,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G6766094321","display_name":null,"funder_award_id":"19ZR1435900","funder_id":"https://openalex.org/F4320309612","funder_display_name":"Natural Science Foundation of Shanghai"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309549","display_name":"University of Houston","ror":"https://ror.org/040vwpm13"},{"id":"https://openalex.org/F4320309612","display_name":"Natural Science Foundation of Shanghai","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2965615220.pdf","grobid_xml":"https://content.openalex.org/works/W2965615220.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1533861849","https://openalex.org/W1677182931","https://openalex.org/W1836465849","https://openalex.org/W1965309615","https://openalex.org/W1966580635","https://openalex.org/W1988386267","https://openalex.org/W1997565609","https://openalex.org/W2029316659","https://openalex.org/W2043665634","https://openalex.org/W2049189005","https://openalex.org/W2062964394","https://openalex.org/W2090424610","https://openalex.org/W2136251662","https://openalex.org/W2172010943","https://openalex.org/W2183341477","https://openalex.org/W2320738207","https://openalex.org/W2412588858","https://openalex.org/W2500751094","https://openalex.org/W2522698497","https://openalex.org/W2564755587","https://openalex.org/W2572303978","https://openalex.org/W2758810255","https://openalex.org/W2764276316","https://openalex.org/W2772163965","https://openalex.org/W2791006446","https://openalex.org/W2822065499","https://openalex.org/W2894165434","https://openalex.org/W2963495494","https://openalex.org/W2963977677","https://openalex.org/W4320339642","https://openalex.org/W6631190155","https://openalex.org/W6682137061"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W3099765033","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W2964954556","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"The":[0,55,78,135],"connection":[1],"structure":[2,42],"in":[3,23,62],"the":[4,14,24,75,104,110,113,128,160,164],"convolutional":[5,35,117,121],"layers":[6],"of":[7,16,106,130,178],"most":[8],"deep":[9,171],"learning-based":[10,172],"algorithms":[11],"used":[12,44],"for":[13,52,74],"classification":[15,166],"hyperspectral":[17],"images":[18],"(HSIs)":[19],"has":[20],"typically":[21],"been":[22],"forward":[25],"direction.":[26],"In":[27],"this":[28],"study,":[29],"an":[30,69,72,98],"end-to-end":[31],"alternately":[32],"updated":[33,60],"spectral\u2013spatial":[34],"network":[36,123],"(AUSSC)":[37],"with":[38,174],"a":[39,175],"recurrent":[40],"feedback":[41],"is":[43,95],"to":[45,102,126],"learn":[46],"refined":[47],"spectral":[48,82],"and":[49,71,83,132,154],"spatial":[50,84],"features":[51,85,107],"HSI":[53,142,165],"classification.":[54],"proposed":[56,136,161],"AUSSC":[57,79,114,162],"includes":[58],"alternating":[59],"blocks":[61],"which":[63],"each":[64],"layer":[65],"serves":[66],"as":[67,97,145],"both":[68],"input":[70],"output":[73],"other":[76,120],"layers.":[77],"can":[80],"refine":[81],"many":[86],"times":[87],"under":[88],"fixed":[89],"parameters.":[90],"A":[91],"center":[92],"loss":[93],"function":[94,101],"introduced":[96],"auxiliary":[99],"objective":[100],"improve":[103],"discrimination":[105],"acquired":[108],"by":[109,169],"model.":[111],"Additionally,":[112],"utilizes":[115],"smaller":[116],"kernels":[118],"than":[119],"neural":[122],"(CNN)-based":[124],"methods":[125,173],"reduce":[127],"number":[129,177],"parameters":[131],"alleviate":[133],"overfitting.":[134],"method":[137],"was":[138],"implemented":[139],"on":[140],"four":[141],"data":[143],"sets,":[144],"follows:":[146],"Indian":[147],"Pines,":[148],"Kennedy":[149],"Space":[150],"Center,":[151],"Salinas":[152],"Scene,":[153],"Houston.":[155],"Experimental":[156],"results":[157],"demonstrated":[158],"that":[159],"outperformed":[163],"accuracy":[167],"obtained":[168],"state-of-the-art":[170],"small":[176],"training":[179],"samples.":[180]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
