{"id":"https://openalex.org/W2950266692","doi":"https://doi.org/10.1109/tgrs.2019.2918080","title":"Visual Attention-Driven Hyperspectral Image Classification","display_name":"Visual Attention-Driven Hyperspectral Image Classification","publication_year":2019,"publication_date":"2019-06-12","ids":{"openalex":"https://openalex.org/W2950266692","doi":"https://doi.org/10.1109/tgrs.2019.2918080","mag":"2950266692"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2019.2918080","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2019.2918080","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/A5039673511","display_name":"Juan M. Haut","orcid":"https://orcid.org/0000-0001-6701-961X"},"institutions":[{"id":"https://openalex.org/I80606768","display_name":"Universidad de Extremadura","ror":"https://ror.org/0174shg90","country_code":"ES","type":"education","lineage":["https://openalex.org/I80606768"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Juan Mario Haut","raw_affiliation_strings":["Hyperspectral Computing Laboratory, Escuela Polit\u00e9cnica, University of Extremadura, C\u00e1ceres, Spain"],"affiliations":[{"raw_affiliation_string":"Hyperspectral Computing Laboratory, Escuela Polit\u00e9cnica, University of Extremadura, C\u00e1ceres, Spain","institution_ids":["https://openalex.org/I80606768"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046123228","display_name":"Mercedes E. Paoletti","orcid":"https://orcid.org/0000-0003-1030-3729"},"institutions":[{"id":"https://openalex.org/I80606768","display_name":"Universidad de Extremadura","ror":"https://ror.org/0174shg90","country_code":"ES","type":"education","lineage":["https://openalex.org/I80606768"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Mercedes E. Paoletti","raw_affiliation_strings":["Hyperspectral Computing Laboratory, Escuela Polit\u00e9cnica, University of Extremadura, C\u00e1ceres, Spain"],"affiliations":[{"raw_affiliation_string":"Hyperspectral Computing Laboratory, Escuela Polit\u00e9cnica, University of Extremadura, C\u00e1ceres, Spain","institution_ids":["https://openalex.org/I80606768"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010624980","display_name":"Javier Plaza","orcid":"https://orcid.org/0000-0002-2384-9141"},"institutions":[{"id":"https://openalex.org/I80606768","display_name":"Universidad de Extremadura","ror":"https://ror.org/0174shg90","country_code":"ES","type":"education","lineage":["https://openalex.org/I80606768"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Javier Plaza","raw_affiliation_strings":["Hyperspectral Computing Laboratory, Escuela Polit\u00e9cnica, University of Extremadura, C\u00e1ceres, Spain"],"affiliations":[{"raw_affiliation_string":"Hyperspectral Computing Laboratory, Escuela Polit\u00e9cnica, University of Extremadura, C\u00e1ceres, Spain","institution_ids":["https://openalex.org/I80606768"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054292278","display_name":"Antonio Plaza","orcid":"https://orcid.org/0000-0002-9613-1659"},"institutions":[{"id":"https://openalex.org/I80606768","display_name":"Universidad de Extremadura","ror":"https://ror.org/0174shg90","country_code":"ES","type":"education","lineage":["https://openalex.org/I80606768"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Antonio Plaza","raw_affiliation_strings":["Hyperspectral Computing Laboratory, Escuela Polit\u00e9cnica, University of Extremadura, C\u00e1ceres, Spain"],"affiliations":[{"raw_affiliation_string":"Hyperspectral Computing Laboratory, Escuela Polit\u00e9cnica, University of Extremadura, C\u00e1ceres, Spain","institution_ids":["https://openalex.org/I80606768"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100362041","display_name":"Jun Li","orcid":"https://orcid.org/0000-0003-1613-9448"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Li","raw_affiliation_strings":["Guangdong Provincial Key Laboratory of Urbanization and Geosimulation, Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Provincial Key Laboratory of Urbanization and Geosimulation, Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5039673511"],"corresponding_institution_ids":["https://openalex.org/I80606768"],"apc_list":null,"apc_paid":null,"fwci":25.5639,"has_fulltext":false,"cited_by_count":242,"citation_normalized_percentile":{"value":0.99693356,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"57","issue":"10","first_page":"8065","last_page":"8080"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.98580002784729,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9836999773979187,"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/computer-science","display_name":"Computer science","score":0.8387250900268555},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8274496793746948},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7368369102478027},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6172645688056946},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5678509473800659},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5521242022514343},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5127514600753784},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4437444806098938},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.43577465415000916},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4289408326148987},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4124453365802765},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4119633436203003},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2820536494255066}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8387250900268555},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8274496793746948},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7368369102478027},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6172645688056946},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5678509473800659},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5521242022514343},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5127514600753784},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4437444806098938},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.43577465415000916},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4289408326148987},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4124453365802765},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4119633436203003},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2820536494255066},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","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.2019.2918080","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2019.2918080","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":109,"referenced_works":["https://openalex.org/W1026270304","https://openalex.org/W1484210532","https://openalex.org/W1491705651","https://openalex.org/W1522301498","https://openalex.org/W1533861849","https://openalex.org/W1665214252","https://openalex.org/W1799366690","https://openalex.org/W1836465849","https://openalex.org/W1928278792","https://openalex.org/W1928906481","https://openalex.org/W1963816830","https://openalex.org/W1965309615","https://openalex.org/W1974083340","https://openalex.org/W1982427174","https://openalex.org/W1990690548","https://openalex.org/W1990895816","https://openalex.org/W2001298023","https://openalex.org/W2010797000","https://openalex.org/W2014327494","https://openalex.org/W2022470997","https://openalex.org/W2024938108","https://openalex.org/W2029316659","https://openalex.org/W2043646035","https://openalex.org/W2053506196","https://openalex.org/W2056522964","https://openalex.org/W2069231830","https://openalex.org/W2076063813","https://openalex.org/W2087263574","https://openalex.org/W2090424610","https://openalex.org/W2101711129","https://openalex.org/W2103212315","https://openalex.org/W2103304742","https://openalex.org/W2104269704","https://openalex.org/W2120294666","https://openalex.org/W2121338139","https://openalex.org/W2122524329","https://openalex.org/W2128272608","https://openalex.org/W2132648706","https://openalex.org/W2136251662","https://openalex.org/W2136922672","https://openalex.org/W2138583748","https://openalex.org/W2146987667","https://openalex.org/W2153747028","https://openalex.org/W2158548804","https://openalex.org/W2161815745","https://openalex.org/W2194775991","https://openalex.org/W2206278467","https://openalex.org/W2274287116","https://openalex.org/W2302255633","https://openalex.org/W2316226477","https://openalex.org/W2401231614","https://openalex.org/W2412588858","https://openalex.org/W2465503420","https://openalex.org/W2472919595","https://openalex.org/W2500751094","https://openalex.org/W2532852010","https://openalex.org/W2542055599","https://openalex.org/W2546152025","https://openalex.org/W2548776929","https://openalex.org/W2549139847","https://openalex.org/W2565258258","https://openalex.org/W2593560791","https://openalex.org/W2603834682","https://openalex.org/W2622826443","https://openalex.org/W2738901395","https://openalex.org/W2752223653","https://openalex.org/W2764276316","https://openalex.org/W2772452219","https://openalex.org/W2782517596","https://openalex.org/W2788311693","https://openalex.org/W2790128178","https://openalex.org/W2801717361","https://openalex.org/W2807621615","https://openalex.org/W2809113079","https://openalex.org/W2809907875","https://openalex.org/W2810821452","https://openalex.org/W2822065499","https://openalex.org/W2888119354","https://openalex.org/W2889943009","https://openalex.org/W2892075618","https://openalex.org/W2894165434","https://openalex.org/W2897395600","https://openalex.org/W2898381489","https://openalex.org/W2898682679","https://openalex.org/W2919115771","https://openalex.org/W2949117887","https://openalex.org/W2950621961","https://openalex.org/W2962856599","https://openalex.org/W2962971773","https://openalex.org/W2963113244","https://openalex.org/W2963495494","https://openalex.org/W2964121744","https://openalex.org/W2964137095","https://openalex.org/W2964350391","https://openalex.org/W3015571647","https://openalex.org/W3105255022","https://openalex.org/W6626481562","https://openalex.org/W6628927728","https://openalex.org/W6631190155","https://openalex.org/W6631943919","https://openalex.org/W6637242042","https://openalex.org/W6638444622","https://openalex.org/W6638667902","https://openalex.org/W6640376812","https://openalex.org/W6694260854","https://openalex.org/W6728721834","https://openalex.org/W6743593290","https://openalex.org/W6755328228","https://openalex.org/W6786371059"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2070598848","https://openalex.org/W3034375524","https://openalex.org/W2027399350","https://openalex.org/W2060875994","https://openalex.org/W2044184146","https://openalex.org/W2019190440","https://openalex.org/W3034864990","https://openalex.org/W4230131218"],"abstract_inverted_index":{"Deep":[0],"neural":[1,6],"networks":[2,7,11],"(DNNs),":[3],"including":[4],"convolutional":[5],"(CNNs)":[8],"and":[9],"residual":[10],"(ResNets)":[12],"models,":[13],"are":[14],"able":[15],"to":[16,108,113,138,143,163,172,208],"learn":[17],"abstract":[18],"representations":[19],"from":[20,58],"the":[21,48,51,56,68,72,76,104,130,146,151,164,168,174,193],"input":[22],"data":[23,57,117,189],"by":[24,71,91,167],"considering":[25],"a":[26,59,124,139,158],"deep":[27,195],"hierarchy":[28],"of":[29,38,50,55,67,75,80,203],"layers":[30,74],"that":[31,160,192],"perform":[32],"advanced":[33],"feature":[34],"extraction.":[35],"The":[36],"combination":[37],"these":[39],"models":[40],"with":[41,47],"visual":[42,60,110,126],"attention":[43,111,136,196],"techniques":[44],"can":[45],"assist":[46],"identification":[49],"most":[52,175],"representative":[53],"parts":[54],"standpoint,":[61],"obtained":[62,166],"through":[63],"more":[64],"detailed":[65],"filtering":[66],"features":[69,165],"extracted":[70],"operational":[73],"network.":[77],"This":[78],"is":[79,161],"significant":[81],"interest":[82],"for":[83,129,178],"analyzing":[84],"remotely":[85,114],"sensed":[86,115],"hyperspectral":[87],"images":[88],"(HSIs),":[89],"characterized":[90],"their":[92],"very":[93],"high":[94],"spectral":[95],"dimensionality.":[96],"However,":[97],"few":[98],"efforts":[99],"have":[100],"been":[101],"conducted":[102,183],"in":[103,106,141,150,170,201],"literature":[105],"order":[107,142,171],"adapt":[109],"methods":[112],"HSI":[116,131,188],"analysis.":[118],"In":[119],"this":[120],"paper,":[121],"we":[122,134],"introduce":[123],"new":[125],"attention-driven":[127],"technique":[128],"classification.":[132],"Specifically,":[133],"incorporate":[135],"mechanisms":[137],"ResNet":[140],"better":[144],"characterize":[145],"spectral-spatial":[147],"information":[148],"contained":[149],"data.":[152],"Our":[153,181],"newly":[154],"proposed":[155,194],"method":[156],"calculates":[157],"mask":[159],"applied":[162],"network":[169],"identify":[173],"desirable":[176],"ones":[177],"classification":[179,204],"purposes.":[180],"experiments,":[182],"using":[184],"four":[185],"widely":[186],"used":[187],"sets,":[190],"reveal":[191],"model":[197],"provides":[198],"competitive":[199],"advantages":[200],"terms":[202],"accuracy":[205],"when":[206],"compared":[207],"other":[209],"state-of-the-art":[210],"methods.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":26},{"year":2024,"cited_by_count":25},{"year":2023,"cited_by_count":46},{"year":2022,"cited_by_count":47},{"year":2021,"cited_by_count":56},{"year":2020,"cited_by_count":34},{"year":2019,"cited_by_count":4}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
