{"id":"https://openalex.org/W2768309288","doi":"https://doi.org/10.1109/jstars.2017.2767185","title":"Classification of Hyperspectral Images by Gabor Filtering Based Deep Network","display_name":"Classification of Hyperspectral Images by Gabor Filtering Based Deep Network","publication_year":2017,"publication_date":"2017-11-22","ids":{"openalex":"https://openalex.org/W2768309288","doi":"https://doi.org/10.1109/jstars.2017.2767185","mag":"2768309288"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2017.2767185","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstars.2017.2767185","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":false,"is_in_doaj":true,"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"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/A5057514965","display_name":"Xudong Kang","orcid":"https://orcid.org/0000-0002-3807-2531"},"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":"Xudong Kang","raw_affiliation_strings":["Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-3807-2531","affiliations":[{"raw_affiliation_string":"Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chengchao 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":"Chengchao Li","raw_affiliation_strings":["Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-0585-9848","affiliations":[{"raw_affiliation_string":"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":["Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112748650","display_name":"Hui Lin","orcid":null},"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":"Hui Lin","raw_affiliation_strings":["Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0003-2351-4461","affiliations":[{"raw_affiliation_string":"Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I16609230"],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":null,"fwci":14.3292,"has_fulltext":false,"cited_by_count":149,"citation_normalized_percentile":{"value":0.98916945,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"11","issue":"4","first_page":"1166","last_page":"1178"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9952999949455261,"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":0.9696999788284302,"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":0.8553639650344849},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8396859169006348},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8210532069206238},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7705472111701965},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7565283179283142},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5529912114143372},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.44286543130874634},{"id":"https://openalex.org/keywords/gabor-filter","display_name":"Gabor filter","score":0.43352869153022766},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4232952892780304},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.383232980966568}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8553639650344849},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8396859169006348},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8210532069206238},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7705472111701965},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7565283179283142},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5529912114143372},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.44286543130874634},{"id":"https://openalex.org/C2779883129","wikidata":"https://www.wikidata.org/wiki/Q2447890","display_name":"Gabor filter","level":3,"score":0.43352869153022766},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4232952892780304},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.383232980966568}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jstars.2017.2767185","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstars.2017.2767185","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":false,"is_in_doaj":true,"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8694044906","display_name":null,"funder_award_id":"61601179","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":47,"referenced_works":["https://openalex.org/W1694520421","https://openalex.org/W1939429412","https://openalex.org/W1967649788","https://openalex.org/W1990895816","https://openalex.org/W1998808035","https://openalex.org/W2000359198","https://openalex.org/W2002849025","https://openalex.org/W2005106632","https://openalex.org/W2018482939","https://openalex.org/W2029316659","https://openalex.org/W2031802401","https://openalex.org/W2041100636","https://openalex.org/W2052160904","https://openalex.org/W2053615857","https://openalex.org/W2059217921","https://openalex.org/W2083541351","https://openalex.org/W2085633292","https://openalex.org/W2090424610","https://openalex.org/W2092869901","https://openalex.org/W2096127742","https://openalex.org/W2097308346","https://openalex.org/W2097915756","https://openalex.org/W2102236241","https://openalex.org/W2105386417","https://openalex.org/W2106277226","https://openalex.org/W2136251662","https://openalex.org/W2145035344","https://openalex.org/W2151665594","https://openalex.org/W2152057649","https://openalex.org/W2153635508","https://openalex.org/W2158400785","https://openalex.org/W2160815625","https://openalex.org/W2163886442","https://openalex.org/W2165731615","https://openalex.org/W2166923144","https://openalex.org/W2257669061","https://openalex.org/W2296756268","https://openalex.org/W2315347323","https://openalex.org/W2346557146","https://openalex.org/W2519307493","https://openalex.org/W2547846938","https://openalex.org/W2565258258","https://openalex.org/W2572303978","https://openalex.org/W2740976805","https://openalex.org/W2754507318","https://openalex.org/W2761917471","https://openalex.org/W4231927828"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W2072166414","https://openalex.org/W4310873165","https://openalex.org/W1585144779","https://openalex.org/W2127352224","https://openalex.org/W2139727660"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"a":[3,72],"novel":[4],"spectral-spatial":[5],"classification":[6,103,148,169,174],"method":[7,164],"based":[8],"on":[9,27,155],"Gabor":[10,19,25,52],"filtering":[11,26],"and":[12,48,54,129,142],"deep":[13,66,76,106,136],"network":[14,77,137],"(GFDN)":[15],"is":[16,95,115],"proposed.":[17],"First,":[18],"features":[20,53,56,67,81],"are":[21,57,68],"extracted":[22],"by":[23,70],"performing":[24],"the":[28,34,41,51,62,79,87,102,127,132,135,162],"first":[29],"three":[30],"principal":[31],"components":[32],"of":[33,45,89,92,111,134,150,173],"hyperspectral":[35,93,158],"image,":[36],"which":[37,99,144],"can":[38,138,145],"typically":[39],"characterize":[40],"low-level":[42],"spatial":[43],"structures":[44],"different":[46],"orientations":[47],"scales.":[49],"Then,":[50],"spectral":[55],"simply":[58],"stacked":[59,73],"to":[60,117],"form":[61],"fused":[63,80],"features.":[64],"Afterwards,":[65],"captured":[69],"training":[71,90,120],"sparse":[74],"autoencoder":[75],"with":[78],"obtained":[82],"above":[83],"as":[84],"inputs.":[85],"Since":[86],"number":[88],"samples":[91,114],"images":[94],"often":[96],"very":[97],"limited,":[98],"negatively":[100],"affects":[101],"performance":[104],"in":[105,147,171],"learning,":[107],"an":[108],"effective":[109],"way":[110],"constructing":[112],"virtual":[113,130],"designed":[116],"generate":[118],"more":[119],"samples,":[121,131],"automatically.":[122],"By":[123],"jointly":[124],"utilizing":[125],"both":[126],"real":[128,157],"parameters":[133],"be":[139],"better":[140],"trained":[141],"updated,":[143],"result":[146],"results":[149],"higher":[151],"accuracies.":[152,175],"Experiments":[153],"performed":[154],"four":[156],"datasets":[159],"show":[160],"that":[161],"proposed":[163,168],"outperforms":[165],"several":[166],"recently":[167],"methods":[170],"terms":[172]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":24},{"year":2021,"cited_by_count":34},{"year":2020,"cited_by_count":27},{"year":2019,"cited_by_count":22},{"year":2018,"cited_by_count":14}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
