{"id":"https://openalex.org/W3192905226","doi":"https://doi.org/10.3390/rs13163117","title":"Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks","display_name":"Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks","publication_year":2021,"publication_date":"2021-08-06","ids":{"openalex":"https://openalex.org/W3192905226","doi":"https://doi.org/10.3390/rs13163117","mag":"3192905226"},"language":"en","primary_location":{"id":"doi:10.3390/rs13163117","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13163117","pdf_url":"https://www.mdpi.com/2072-4292/13/16/3117/pdf?version=1628258579","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/2072-4292/13/16/3117/pdf?version=1628258579","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074193757","display_name":"Huize Liu","orcid":"https://orcid.org/0000-0002-8662-7757"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huize Liu","raw_affiliation_strings":["Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"],"affiliations":[{"raw_affiliation_string":"Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014326389","display_name":"Ke Wu","orcid":"https://orcid.org/0000-0001-9692-4221"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ke Wu","raw_affiliation_strings":["Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"],"affiliations":[{"raw_affiliation_string":"Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079952696","display_name":"Honggen Xu","orcid":"https://orcid.org/0000-0001-7617-892X"},"institutions":[{"id":"https://openalex.org/I2799486974","display_name":"China Geological Survey","ror":"https://ror.org/04wtq2305","country_code":"CN","type":"other","lineage":["https://openalex.org/I2799486974"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Honggen Xu","raw_affiliation_strings":["Changsha Center of Natural Resources Comprehensive Survey, China Geological Survey, Changsha 410699, China"],"affiliations":[{"raw_affiliation_string":"Changsha Center of Natural Resources Comprehensive Survey, China Geological Survey, Changsha 410699, China","institution_ids":["https://openalex.org/I2799486974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059007878","display_name":"Ying Xu","orcid":"https://orcid.org/0000-0002-2068-1216"},"institutions":[{"id":"https://openalex.org/I4210166112","display_name":"State Key Laboratory of Remote Sensing Science","ror":"https://ror.org/05wzjqa24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210166112"]},{"id":"https://openalex.org/I211433327","display_name":"Ministry of Natural Resources","ror":"https://ror.org/02kxqx159","country_code":"CN","type":"funder","lineage":["https://openalex.org/I211433327","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I4210128742","display_name":"National Satellite Ocean Application Service","ror":"https://ror.org/038fwcw54","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210128742"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Xu","raw_affiliation_strings":["Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China","National Satellite Ocean Application Service, Ministry of Natural Resources, Beijing 100081, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China","institution_ids":["https://openalex.org/I4210166112","https://openalex.org/I211433327"]},{"raw_affiliation_string":"National Satellite Ocean Application Service, Ministry of Natural Resources, Beijing 100081, China","institution_ids":["https://openalex.org/I4210128742","https://openalex.org/I211433327"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5014326389"],"corresponding_institution_ids":["https://openalex.org/I3124059619"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":4.3397,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.95208894,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"13","issue":"16","first_page":"3117","last_page":"3117"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9997000098228455,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9986000061035156,"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":0.9682999849319458,"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":0.8660364151000977},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7049393057823181},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.6302807331085205},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5892854928970337},{"id":"https://openalex.org/keywords/lithology","display_name":"Lithology","score":0.556891679763794},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4908038079738617},{"id":"https://openalex.org/keywords/thermal-infrared","display_name":"Thermal infrared","score":0.4720773696899414},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46730709075927734},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.46393778920173645},{"id":"https://openalex.org/keywords/infrared","display_name":"Infrared","score":0.43824467062950134},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.061780691146850586},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.05504116415977478},{"id":"https://openalex.org/keywords/geochemistry","display_name":"Geochemistry","score":0.052694231271743774}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8660364151000977},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7049393057823181},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6302807331085205},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5892854928970337},{"id":"https://openalex.org/C122792734","wikidata":"https://www.wikidata.org/wiki/Q6538759","display_name":"Lithology","level":2,"score":0.556891679763794},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4908038079738617},{"id":"https://openalex.org/C2984335091","wikidata":"https://www.wikidata.org/wiki/Q11388","display_name":"Thermal infrared","level":3,"score":0.4720773696899414},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46730709075927734},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.46393778920173645},{"id":"https://openalex.org/C158355884","wikidata":"https://www.wikidata.org/wiki/Q11388","display_name":"Infrared","level":2,"score":0.43824467062950134},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.061780691146850586},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.05504116415977478},{"id":"https://openalex.org/C17409809","wikidata":"https://www.wikidata.org/wiki/Q161764","display_name":"Geochemistry","level":1,"score":0.052694231271743774}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs13163117","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13163117","pdf_url":"https://www.mdpi.com/2072-4292/13/16/3117/pdf?version=1628258579","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:2fe570896d104d68a6a2f8bc75f0cb9c","is_oa":true,"landing_page_url":"https://doaj.org/article/2fe570896d104d68a6a2f8bc75f0cb9c","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 13, Iss 16, p 3117 (2021)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/13/16/3117/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs13163117","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 13; Issue 16; Pages: 3117","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs13163117","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13163117","pdf_url":"https://www.mdpi.com/2072-4292/13/16/3117/pdf?version=1628258579","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":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6800000071525574}],"awards":[{"id":"https://openalex.org/G3479671419","display_name":null,"funder_award_id":"62071439","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":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3192905226.pdf","grobid_xml":"https://content.openalex.org/works/W3192905226.grobid-xml"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W1522734439","https://openalex.org/W1967412740","https://openalex.org/W1967782666","https://openalex.org/W1977803272","https://openalex.org/W1983627704","https://openalex.org/W2005353255","https://openalex.org/W2027442956","https://openalex.org/W2084469944","https://openalex.org/W2100921418","https://openalex.org/W2106518202","https://openalex.org/W2136922672","https://openalex.org/W2140103896","https://openalex.org/W2141215107","https://openalex.org/W2169447826","https://openalex.org/W2212207508","https://openalex.org/W2265516356","https://openalex.org/W2471810144","https://openalex.org/W2500751094","https://openalex.org/W2547363876","https://openalex.org/W2547610300","https://openalex.org/W2572303978","https://openalex.org/W2610339468","https://openalex.org/W2757781625","https://openalex.org/W2783165089","https://openalex.org/W2792685313","https://openalex.org/W2890670700","https://openalex.org/W2908624219","https://openalex.org/W2910313531","https://openalex.org/W2914331134","https://openalex.org/W2920641855","https://openalex.org/W2950695236","https://openalex.org/W2982945795","https://openalex.org/W2986306329","https://openalex.org/W2991264163","https://openalex.org/W3004713504","https://openalex.org/W3022140654","https://openalex.org/W3023114228","https://openalex.org/W3023351371","https://openalex.org/W3033351122","https://openalex.org/W3035524050","https://openalex.org/W3085033577","https://openalex.org/W3086507804","https://openalex.org/W3097668537","https://openalex.org/W3107136435","https://openalex.org/W3133131242","https://openalex.org/W3151174548","https://openalex.org/W6642198582","https://openalex.org/W6646387326","https://openalex.org/W6688550160","https://openalex.org/W6771508001"],"related_works":["https://openalex.org/W3184332868","https://openalex.org/W2360806688","https://openalex.org/W273971055","https://openalex.org/W1858933348","https://openalex.org/W2351006740","https://openalex.org/W3157789538","https://openalex.org/W2372640428","https://openalex.org/W4390606651","https://openalex.org/W4405009444","https://openalex.org/W3040982946"],"abstract_inverted_index":{"In":[0,51,117],"recent":[1,118],"decades,":[2],"lithological":[3],"mapping":[4,87],"techniques":[5],"using":[6,17,171],"hyperspectral":[7,25,91,127,169],"remotely":[8],"sensed":[9],"imagery":[10,128],"have":[11,147],"developed":[12],"rapidly.":[13],"The":[14,34],"processing":[15],"chains":[16],"visible-near":[18],"infrared":[19,23,36,93,168],"(VNIR)":[20],"and":[21,48,88,200,207,230,237,251,270],"shortwave":[22],"(SWIR)":[24],"data":[26,94,170],"are":[27,95,105],"proven":[28],"to":[29,63,73,75,110,152,209],"be":[30,64,68],"available":[31],"in":[32,126,157,182],"practice.":[33],"thermal":[35,92,167],"(TIR)":[37],"portion":[38],"of":[39,58,84,114,234,247],"the":[40,53,85,111,210,216,219,240,244,259,264],"electromagnetic":[41],"spectrum":[42],"has":[43,122],"considerable":[44],"potential":[45],"for":[46,90],"mineral":[47],"lithology":[49,76,86,160],"mapping.":[50],"particular,":[52],"abovementioned":[54],"rocks":[55],"at":[56,178],"wavelengths":[57],"8\u201312":[59],"\u03bcm":[60],"were":[61,205,256],"found":[62,81],"discriminative,":[65],"which":[66,104,146,261],"can":[67],"seen":[69],"as":[70],"a":[71,138,172],"characteristic":[72],"apply":[74],"classification.":[77],"Moreover,":[78],"it":[79],"was":[80,164,227],"that":[82,263],"most":[83],"classification":[89,129,161],"still":[96],"carried":[97],"out":[98],"by":[99],"traditional":[100],"spectral":[101],"matching":[102],"methods,":[103],"not":[106],"very":[107],"reliable":[108],"due":[109,151],"complex":[112],"diversity":[113],"geological":[115],"lithology.":[116],"years,":[119],"deep":[120],"learning":[121],"made":[123],"great":[124],"achievements":[125],"feature":[130],"extraction.":[131],"It":[132],"usually":[133],"captures":[134],"abstract":[135],"features":[136],"through":[137],"multilayer":[139],"network,":[140],"especially":[141],"convolutional":[142],"neural":[143],"networks":[144],"(CNNs),":[145],"received":[148],"more":[149],"attention":[150],"their":[153],"unique":[154],"advantages.":[155],"Hence,":[156],"this":[158],"paper,":[159],"with":[162,258],"CNNs":[163,226],"tested":[165],"on":[166,225],"Thermal":[173],"Airborne":[174],"Spectrographic":[175],"Imager":[176],"(TASI)":[177],"three":[179,217],"small":[180],"sites":[181],"Liuyuan,":[183],"Gansu":[184],"Province,":[185],"China.":[186],"Three":[187],"different":[188],"CNN":[189,193,197,202],"algorithms,":[190],"including":[191],"one-dimensional":[192],"(1-D":[194],"CNN),":[195,204],"two-dimensional":[196],"(2-D":[198],"CNN)":[199],"three-dimensional":[201],"(3-D":[203],"implemented":[206],"compared":[208],"six":[211],"relevant":[212],"state-of-the-art":[213],"methods.":[214,273],"At":[215],"sites,":[218],"maximum":[220],"overall":[221],"accuracy":[222,246,269],"(OA)":[223],"based":[224],"94.70%,":[228],"96.47%":[229],"98.56%,":[231],"representing":[232],"improvements":[233],"22.58%,":[235],"25.93%":[236],"16.88%":[238],"over":[239],"worst":[241],"OA.":[242],"Meanwhile,":[243],"average":[245],"all":[248],"classes":[249],"(AA)":[250],"kappa":[252],"coefficient":[253],"(kappa)":[254],"value":[255],"consistent":[257],"OA,":[260],"confirmed":[262],"focal":[265],"method":[266],"effectively":[267],"improved":[268],"outperformed":[271],"other":[272]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2}],"updated_date":"2026-04-14T08:04:32.555800","created_date":"2021-08-16T00:00:00"}
