{"id":"https://openalex.org/W4401879117","doi":"https://doi.org/10.1109/jstars.2024.3449436","title":"Lithological Classification by Hyperspectral Remote Sensing Images Based on Double-Branch Multiscale Dual-Attention Network","display_name":"Lithological Classification by Hyperspectral Remote Sensing Images Based on Double-Branch Multiscale Dual-Attention Network","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4401879117","doi":"https://doi.org/10.1109/jstars.2024.3449436"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2024.3449436","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2024.3449436","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":true,"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":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/jstars.2024.3449436","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077996365","display_name":"Hanhu Liu","orcid":"https://orcid.org/0000-0002-2928-7995"},"institutions":[{"id":"https://openalex.org/I31595395","display_name":"Chengdu University of Technology","ror":"https://ror.org/05pejbw21","country_code":"CN","type":"education","lineage":["https://openalex.org/I31595395"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hanhu Liu","raw_affiliation_strings":["College of Geography and Planning, Chengdu University of Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"College of Geography and Planning, Chengdu University of Technology, Chengdu, China","institution_ids":["https://openalex.org/I31595395"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114269740","display_name":"Heng Zhang","orcid":"https://orcid.org/0009-0001-3127-7956"},"institutions":[{"id":"https://openalex.org/I31595395","display_name":"Chengdu University of Technology","ror":"https://ror.org/05pejbw21","country_code":"CN","type":"education","lineage":["https://openalex.org/I31595395"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heng Zhang","raw_affiliation_strings":["College of Geography and Planning, Chengdu University of Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"College of Geography and Planning, Chengdu University of Technology, Chengdu, China","institution_ids":["https://openalex.org/I31595395"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074023635","display_name":"Ronghao Yang","orcid":"https://orcid.org/0000-0002-3394-090X"},"institutions":[{"id":"https://openalex.org/I31595395","display_name":"Chengdu University of Technology","ror":"https://ror.org/05pejbw21","country_code":"CN","type":"education","lineage":["https://openalex.org/I31595395"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ronghao Yang","raw_affiliation_strings":["College of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"College of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu, China","institution_ids":["https://openalex.org/I31595395"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5077996365"],"corresponding_institution_ids":["https://openalex.org/I31595395"],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":{"value":1250,"currency":"USD","value_usd":1250},"fwci":2.5584,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.90642492,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"17","issue":null,"first_page":"14726","last_page":"14741"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9993000030517578,"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":0.9993000030517578,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9884999990463257,"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":0.8929771184921265},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.6441892385482788},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6354957222938538},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.6142358183860779},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5282472968101501},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4075452387332916},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3605312705039978},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.32994556427001953}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8929771184921265},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.6441892385482788},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6354957222938538},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6142358183860779},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5282472968101501},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4075452387332916},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3605312705039978},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.32994556427001953},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jstars.2024.3449436","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2024.3449436","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":true,"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"},{"id":"pmh:oai:doaj.org/article:604d781a88b74b9ebacfb008d6a944ea","is_oa":true,"landing_page_url":"https://doaj.org/article/604d781a88b74b9ebacfb008d6a944ea","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":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 14726-14741 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/jstars.2024.3449436","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2024.3449436","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":true,"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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1778870018","https://openalex.org/W2396056163","https://openalex.org/W2764276316","https://openalex.org/W2792332881","https://openalex.org/W2822065499","https://openalex.org/W2948157022","https://openalex.org/W2955058313","https://openalex.org/W2991264163","https://openalex.org/W3002734626","https://openalex.org/W3102274762","https://openalex.org/W3111935347","https://openalex.org/W3125860323","https://openalex.org/W3140678646","https://openalex.org/W3192905226","https://openalex.org/W3192932942","https://openalex.org/W3201500910","https://openalex.org/W3205965083","https://openalex.org/W3214821343","https://openalex.org/W4207037158","https://openalex.org/W4281568429","https://openalex.org/W4306157622","https://openalex.org/W4312085175","https://openalex.org/W4316664563","https://openalex.org/W4385399695","https://openalex.org/W4386399715","https://openalex.org/W4388283871","https://openalex.org/W4388634330","https://openalex.org/W4389776891","https://openalex.org/W4392397276","https://openalex.org/W4392763170","https://openalex.org/W4399432959"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2404757046","https://openalex.org/W2070598848","https://openalex.org/W2385371209","https://openalex.org/W4250051149","https://openalex.org/W2083270190"],"abstract_inverted_index":{"Although":[0],"many":[1],"scholars":[2],"have":[3,9],"realized":[4],"that":[5,132,169,188,215,232],"deep":[6,126],"learning":[7,124,127,229],"methods":[8],"great":[10],"advantages":[11],"in":[12,74],"hyperspectral":[13,37],"lithology":[14,139],"classification":[15,140,194],"now,":[16],"most":[17],"of":[18,160,198],"them":[19],"use":[20],"simple":[21],"convolutional":[22],"neural":[23],"networks":[24],"for":[25,113],"discussion,":[26],"which":[27],"are":[28,99,111],"difficult":[29],"to":[30,78,89,101,180,213],"effectively":[31,174],"extract":[32,79,103],"spectral":[33,67,76,82,91],"sequence":[34],"information":[35,83],"from":[36,84,204],"image":[38],"(HSI).":[39],"To":[40],"address":[41],"this":[42],"issue,":[43],"we":[44],"propose":[45],"a":[46],"double-branch":[47],"multiscale":[48,66,81],"dual-attention":[49],"(DBMSDA)":[50],"network":[51,63,119,135,172,191,218,235],"using":[52],"two":[53,110],"HSIs":[54,197,203],"multiple":[55],"different":[56,205],"rock":[57,200],"types":[58,201],"as":[59],"data":[60,206],"sources.":[61,207],"This":[62],"utilizes":[64],"the":[65,75,87,94,109,117,133,137,147,165,170,184,189,216,233,242],"residual":[68],"self-attention":[69],"structure":[70],"and":[71,107,125,142,149,154,162,177,202,231,239],"dense":[72,97],"connections":[73,98],"branch":[77],"diagnostic":[80,104],"HSIs,":[85],"improving":[86],"ability":[88],"reuse":[90],"information.":[92],"In":[93],"spatial":[95,105],"branch,":[96],"used":[100],"fully":[102],"information,":[106],"finally,":[108],"fused":[112],"classification.":[114],"We":[115],"compare":[116],"DBMSDA":[118,134,171,190,217,234],"with":[120,226],"several":[121],"traditional":[122,227],"machine":[123,228],"methods.":[128,182],"Experimental":[129],"results":[130],"show":[131],"has":[136,192,236],"best":[138],"performance":[141,195],"robustness.":[143],"Its":[144],"OA":[145],"on":[146,196,221],"ZCD1":[148],"HCD2":[150],"datasets":[151],"is":[152,178],"85.08%":[153],"90.80%,":[155],"respectively,":[156],"representing":[157],"an":[158],"improvement":[159],"5.95%":[161],"2.4%":[163],"over":[164],"lowest":[166],"OA,":[167],"confirming":[168],"can":[173],"improve":[175],"accuracy":[176],"superior":[179],"other":[181],"Meanwhile,":[183],"experiment":[185],"also":[186],"showed":[187],"good":[193],"various":[199],"Finally,":[208],"ablation":[209],"experiments":[210],"were":[211],"conducted":[212],"demonstrate":[214],"relies":[219],"more":[220],"large":[222],"training":[223,245],"samples":[224],"compared":[225],"models,":[230],"better":[237],"applicability":[238],"stability":[240],"under":[241],"same":[243],"sufficient":[244],"samples.":[246]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}