{"id":"https://openalex.org/W2792332881","doi":"https://doi.org/10.1109/tgrs.2018.2794326","title":"Hyperspectral Image Classification With Deep Feature Fusion Network","display_name":"Hyperspectral Image Classification With Deep Feature Fusion Network","publication_year":2018,"publication_date":"2018-02-07","ids":{"openalex":"https://openalex.org/W2792332881","doi":"https://doi.org/10.1109/tgrs.2018.2794326","mag":"2792332881"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2018.2794326","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2018.2794326","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/A5019995639","display_name":"Weiwei Song","orcid":"https://orcid.org/0000-0001-5089-4127"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weiwei Song","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067097659","display_name":"Shutao Li","orcid":"https://orcid.org/0000-0002-0585-9848"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shutao Li","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-0585-9848","affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065061505","display_name":"Leyuan Fang","orcid":"https://orcid.org/0000-0003-2351-4461"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Leyuan Fang","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0003-2351-4461","affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102716602","display_name":"Ting Lu","orcid":"https://orcid.org/0000-0001-8415-0185"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Lu","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0001-6744-2186","affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5019995639"],"corresponding_institution_ids":["https://openalex.org/I16609230"],"apc_list":null,"apc_paid":null,"fwci":47.6547,"has_fulltext":false,"cited_by_count":563,"citation_normalized_percentile":{"value":0.9989183,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"56","issue":"6","first_page":"3173","last_page":"3184"},"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":0.993399977684021,"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":0.9900000095367432,"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/overfitting","display_name":"Overfitting","score":0.8732240200042725},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8080623149871826},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7864095568656921},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7583451271057129},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7309916019439697},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.712676465511322},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6522122025489807},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6486182808876038},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5052066445350647},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.494956910610199},{"id":"https://openalex.org/keywords/deep-belief-network","display_name":"Deep belief network","score":0.4259127676486969},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42506420612335205},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4249933660030365},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3770524561405182},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2896891236305237},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.07299908995628357}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8732240200042725},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8080623149871826},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7864095568656921},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7583451271057129},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7309916019439697},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.712676465511322},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6522122025489807},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6486182808876038},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5052066445350647},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.494956910610199},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.4259127676486969},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42506420612335205},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4249933660030365},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3770524561405182},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2896891236305237},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.07299908995628357},{"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":1,"locations":[{"id":"doi:10.1109/tgrs.2018.2794326","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2018.2794326","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":[{"id":"https://metadata.un.org/sdg/10","score":0.7400000095367432,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G898426514","display_name":null,"funder_award_id":"61520106001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W1491705651","https://openalex.org/W1596986901","https://openalex.org/W1836465849","https://openalex.org/W1903029394","https://openalex.org/W1928626817","https://openalex.org/W1939429412","https://openalex.org/W1990895816","https://openalex.org/W2001298023","https://openalex.org/W2029316659","https://openalex.org/W2035057253","https://openalex.org/W2045095960","https://openalex.org/W2052160904","https://openalex.org/W2059089906","https://openalex.org/W2059497048","https://openalex.org/W2085529604","https://openalex.org/W2085625911","https://openalex.org/W2090424610","https://openalex.org/W2097117768","https://openalex.org/W2097915756","https://openalex.org/W2101365302","https://openalex.org/W2102605133","https://openalex.org/W2108597246","https://openalex.org/W2110798204","https://openalex.org/W2112796928","https://openalex.org/W2113464037","https://openalex.org/W2114819256","https://openalex.org/W2136251662","https://openalex.org/W2136922672","https://openalex.org/W2153635508","https://openalex.org/W2158400785","https://openalex.org/W2163605009","https://openalex.org/W2164330327","https://openalex.org/W2166923144","https://openalex.org/W2179290474","https://openalex.org/W2194775991","https://openalex.org/W2239589426","https://openalex.org/W2314785379","https://openalex.org/W2315347323","https://openalex.org/W2342880667","https://openalex.org/W2345128667","https://openalex.org/W2412588858","https://openalex.org/W2500751094","https://openalex.org/W2512351403","https://openalex.org/W2518831014","https://openalex.org/W2538244214","https://openalex.org/W2548791488","https://openalex.org/W2582369608","https://openalex.org/W2587790406","https://openalex.org/W2592962403","https://openalex.org/W2595902385","https://openalex.org/W2603422184","https://openalex.org/W2613575128","https://openalex.org/W2620429297","https://openalex.org/W2620858446","https://openalex.org/W2750085043","https://openalex.org/W2768211636","https://openalex.org/W2963037989","https://openalex.org/W6635815790","https://openalex.org/W6676481782","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W3040691452","https://openalex.org/W2594876342","https://openalex.org/W2624092175","https://openalex.org/W2761785940","https://openalex.org/W3128011703","https://openalex.org/W2742991909"],"abstract_inverted_index":{"Recently,":[0],"deep":[1,17,81,115,130],"learning":[2,97],"has":[3],"been":[4],"introduced":[5,99],"to":[6,26,100,132],"classify":[7],"hyperspectral":[8],"images":[9],"(HSIs)":[10],"and":[11,44,117],"achieved":[12],"good":[13],"performance.":[14],"In":[15,52],"general,":[16],"models":[18],"adopt":[19],"a":[20,80,123,128],"large":[21],"number":[22],"of":[23,114,137,150],"hierarchical":[24,72,152],"layers":[25,104],"extract":[27,133],"features.":[28],"However,":[29],"excessively":[30],"increasing":[31,120],"network":[32,84,116,131],"depth":[33],"will":[34],"result":[35],"in":[36,58],"some":[37],"negative":[38],"effects":[39],"(e.g.,":[40],"overfitting,":[41],"gradient":[42],"vanishing,":[43],"accuracy":[45],"degrading)":[46],"for":[47,88],"conventional":[48],"convolutional":[49,103],"neural":[50],"networks.":[51],"addition,":[53],"the":[54,64,76,92,95,106,112,140,143,148,158,169],"previous":[55],"networks":[56],"used":[57],"HSI":[59,89],"classification":[60,159],"do":[61],"not":[62],"consider":[63],"strong":[65],"complementary":[66],"yet":[67],"correlated":[68],"information":[69],"among":[70],"different":[71,151],"layers.":[73],"To":[74],"address":[75],"above":[77],"two":[78],"issues,":[79],"feature":[82],"fusion":[83],"(DFFN)":[85],"is":[86,98],"proposed":[87,144,170],"classification.":[90],"On":[91,139],"one":[93],"hand,":[94,142],"residual":[96],"optimize":[101],"several":[102],"as":[105],"identity":[107],"mapping,":[108],"which":[109,154],"can":[110,126,155],"ease":[111],"training":[113],"benefit":[118],"from":[119],"depth.":[121],"As":[122],"result,":[124],"we":[125],"build":[127],"very":[129],"more":[134],"discriminative":[135],"features":[136],"HSIs.":[138],"other":[141,173],"DFFN":[145],"model":[146],"fuses":[147],"outputs":[149],"layers,":[153],"further":[156],"improve":[157],"accuracy.":[160],"Experimental":[161],"results":[162],"on":[163],"three":[164],"real":[165],"HSIs":[166],"demonstrate":[167],"that":[168],"method":[171],"outperforms":[172],"competitive":[174],"classifiers.":[175]},"counts_by_year":[{"year":2026,"cited_by_count":9},{"year":2025,"cited_by_count":62},{"year":2024,"cited_by_count":99},{"year":2023,"cited_by_count":86},{"year":2022,"cited_by_count":79},{"year":2021,"cited_by_count":86},{"year":2020,"cited_by_count":74},{"year":2019,"cited_by_count":51},{"year":2018,"cited_by_count":17}],"updated_date":"2026-05-08T15:41:06.802602","created_date":"2025-10-10T00:00:00"}