{"id":"https://openalex.org/W4390232172","doi":"https://doi.org/10.3390/rs16010094","title":"Attention-Guided Fusion and Classification for Hyperspectral and LiDAR Data","display_name":"Attention-Guided Fusion and Classification for Hyperspectral and LiDAR Data","publication_year":2023,"publication_date":"2023-12-25","ids":{"openalex":"https://openalex.org/W4390232172","doi":"https://doi.org/10.3390/rs16010094"},"language":"en","primary_location":{"id":"doi:10.3390/rs16010094","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16010094","pdf_url":"https://www.mdpi.com/2072-4292/16/1/94/pdf?version=1703581614","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/2072-4292/16/1/94/pdf?version=1703581614","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101980929","display_name":"Jing Huang","orcid":"https://orcid.org/0009-0005-4243-9235"},"institutions":[{"id":"https://openalex.org/I43922553","display_name":"Wuhan University of Science and Technology","ror":"https://ror.org/00e4hrk88","country_code":"CN","type":"education","lineage":["https://openalex.org/I43922553"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Huang","raw_affiliation_strings":["Engineering Research Center of Metallurgical Automation and Measurement Technology, Wuhan University of Science and Technology, Wuhan 430081, China"],"affiliations":[{"raw_affiliation_string":"Engineering Research Center of Metallurgical Automation and Measurement Technology, Wuhan University of Science and Technology, Wuhan 430081, China","institution_ids":["https://openalex.org/I43922553"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101638513","display_name":"Yinghao Zhang","orcid":"https://orcid.org/0000-0002-1804-5560"},"institutions":[{"id":"https://openalex.org/I43922553","display_name":"Wuhan University of Science and Technology","ror":"https://ror.org/00e4hrk88","country_code":"CN","type":"education","lineage":["https://openalex.org/I43922553"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinghao Zhang","raw_affiliation_strings":["Engineering Research Center of Metallurgical Automation and Measurement Technology, Wuhan University of Science and Technology, Wuhan 430081, China"],"affiliations":[{"raw_affiliation_string":"Engineering Research Center of Metallurgical Automation and Measurement Technology, Wuhan University of Science and Technology, Wuhan 430081, China","institution_ids":["https://openalex.org/I43922553"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083483870","display_name":"Fang Yang","orcid":"https://orcid.org/0000-0002-8662-9998"},"institutions":[{"id":"https://openalex.org/I43922553","display_name":"Wuhan University of Science and Technology","ror":"https://ror.org/00e4hrk88","country_code":"CN","type":"education","lineage":["https://openalex.org/I43922553"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fang Yang","raw_affiliation_strings":["Engineering Research Center of Metallurgical Automation and Measurement Technology, Wuhan University of Science and Technology, Wuhan 430081, China"],"affiliations":[{"raw_affiliation_string":"Engineering Research Center of Metallurgical Automation and Measurement Technology, Wuhan University of Science and Technology, Wuhan 430081, China","institution_ids":["https://openalex.org/I43922553"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100704809","display_name":"Li Chai","orcid":"https://orcid.org/0000-0002-4331-0565"},"institutions":[{"id":"https://openalex.org/I4391767838","display_name":"State Key Laboratory of Industrial Control Technology","ror":"https://ror.org/03a33a786","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391767838","https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Chai","raw_affiliation_strings":["State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China","institution_ids":["https://openalex.org/I4391767838"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5083483870"],"corresponding_institution_ids":["https://openalex.org/I43922553"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":2.5804,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.91178258,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"16","issue":"1","first_page":"94","last_page":"94"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9976999759674072,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.8076906204223633},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7721835970878601},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6353503465652466},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6141635179519653},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5833512544631958},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5211727023124695},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5203791856765747},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.4715963304042816},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46126702427864075},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4209064245223999},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08577591180801392}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.8076906204223633},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7721835970878601},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6353503465652466},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6141635179519653},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5833512544631958},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5211727023124695},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5203791856765747},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.4715963304042816},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46126702427864075},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4209064245223999},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08577591180801392},{"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:10.3390/rs16010094","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16010094","pdf_url":"https://www.mdpi.com/2072-4292/16/1/94/pdf?version=1703581614","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:a37c6c6b782f488b8fd328b528b4d285","is_oa":true,"landing_page_url":"https://doaj.org/article/a37c6c6b782f488b8fd328b528b4d285","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 16, Iss 1, p 94 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/rs16010094","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16010094","pdf_url":"https://www.mdpi.com/2072-4292/16/1/94/pdf?version=1703581614","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":[{"score":0.800000011920929,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G1046376829","display_name":null,"funder_award_id":"62101392","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":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4390232172.pdf"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W1976416886","https://openalex.org/W2084170803","https://openalex.org/W2128154996","https://openalex.org/W2165796970","https://openalex.org/W2296450878","https://openalex.org/W2346557146","https://openalex.org/W2565258258","https://openalex.org/W2572303978","https://openalex.org/W2606929568","https://openalex.org/W2623518586","https://openalex.org/W2765739551","https://openalex.org/W2792332881","https://openalex.org/W2803946774","https://openalex.org/W2884585870","https://openalex.org/W2890133123","https://openalex.org/W2908968031","https://openalex.org/W2947295162","https://openalex.org/W2983192418","https://openalex.org/W3002674187","https://openalex.org/W3048631361","https://openalex.org/W3088464175","https://openalex.org/W3094484482","https://openalex.org/W3099239430","https://openalex.org/W3102692100","https://openalex.org/W3191036819","https://openalex.org/W3214533654","https://openalex.org/W4200068923","https://openalex.org/W4220709153","https://openalex.org/W4224087782","https://openalex.org/W4236714952","https://openalex.org/W4285134520","https://openalex.org/W4285202680","https://openalex.org/W4312339456","https://openalex.org/W4312989292","https://openalex.org/W4319596938","https://openalex.org/W4328104238","https://openalex.org/W6799336272","https://openalex.org/W6839409146","https://openalex.org/W6840138765","https://openalex.org/W6847273737","https://openalex.org/W6850821523"],"related_works":["https://openalex.org/W4319317934","https://openalex.org/W4406302447","https://openalex.org/W2901265155","https://openalex.org/W2072166414","https://openalex.org/W2956374172","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W4319837668","https://openalex.org/W4308071650","https://openalex.org/W3188333020"],"abstract_inverted_index":{"The":[0,265],"joint":[1,83],"use":[2,84],"of":[3,50,85,114,222,268],"hyperspectral":[4],"image":[5,42],"(HSI)":[6],"and":[7,30,64,87,116,142,164,167,178,189,217,247,273,279],"Light":[8],"Detection":[9],"And":[10],"Ranging":[11],"(LiDAR)":[12],"data":[13,52,166],"has":[14],"been":[15],"widely":[16],"applied":[17],"for":[18,185,236],"land":[19,31,78],"cover":[20,79],"classification":[21,234,258],"because":[22],"it":[23],"can":[24,212],"comprehensively":[25],"represent":[26],"the":[27,40,47,77,82,91,97,111,122,129,139,143,160,169,181,192,197,203,208,219,223,226,232,252,262],"urban":[28],"structures":[29,205],"material":[32],"properties.":[33],"However,":[34],"existing":[35],"methods":[36],"fail":[37],"to":[38,75,109,120,135,138,149,156,180,206],"combine":[39],"different":[41,51],"information":[43],"effectively,":[44],"which":[45,158,211],"limits":[46],"semantic":[48],"relevance":[49],"sources.":[53],"To":[54],"solve":[55],"this":[56,59],"problem,":[57],"in":[58],"paper,":[60],"an":[61],"Attention-guided":[62],"Fusion":[63],"Classification":[65],"framework":[66,255],"based":[67,80],"on":[68,81,241,270],"Convolutional":[69],"Neural":[70],"Network":[71],"(AFC-CNN)":[72],"is":[73,133,147],"proposed":[74,253],"classify":[76],"HSI":[86,163],"LiDAR":[88,126,155,165],"data.":[89,127],"In":[90,191],"feature":[92,174,182,187],"extraction":[93,188],"module,":[94,194],"AFC-CNN":[95,195,254,269],"employs":[96],"three":[98,242],"dimensional":[99],"convolutional":[100],"neural":[101],"network":[102],"(3D-CNN)":[103],"combined":[104],"with":[105,261],"a":[106,118],"multi-scale":[107],"structure":[108],"extract":[110,121],"spatial-spectral":[112],"features":[113,124,228],"HSI,":[115,157],"uses":[117],"2D-CNN":[119],"spatial":[123,152],"from":[125,154],"Simultaneously,":[128],"spectral":[130,140],"attention":[131,145],"mechanism":[132,146],"adopted":[134],"assign":[136],"weights":[137,153],"channels,":[141],"cross":[144],"introduced":[148],"impart":[150],"significant":[151],"enhance":[159],"interaction":[161],"between":[162],"leverage":[168],"fusion":[170,183,193],"information.":[171],"Then":[172],"two":[173],"branches":[175],"are":[176,229,276],"concatenated":[177],"transferred":[179],"module":[184,235],"higher-level":[186],"fusion.":[190],"adopts":[196],"depth":[198],"separable":[199],"convolution":[200],"connected":[201],"through":[202],"residual":[204],"obtain":[207],"advanced":[209],"features,":[210],"help":[213],"reduce":[214],"computational":[215],"complexity":[216],"improve":[218],"fitting":[220],"ability":[221],"model.":[224],"Finally,":[225],"fused":[227],"sent":[230],"into":[231],"linear":[233],"final":[237],"classification.":[238],"Experimental":[239],"results":[240],"datasets,":[243],"i.e.,":[244],"Houston,":[245,271],"MUUFL":[246,272],"Trento":[248,274],"datasets":[249,275],"show":[250],"that":[251],"achieves":[256],"better":[257],"accuracy":[259,267],"compared":[260],"state-of-the-art":[263],"algorithms.":[264],"overall":[266],"94.2%,":[277],"95.3%":[278],"99.5%,":[280],"respectively.":[281]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-14T08:43:22.919905","created_date":"2025-10-10T00:00:00"}
