{"id":"https://openalex.org/W4387987050","doi":"https://doi.org/10.1109/lgrs.2023.3328139","title":"A New Approach for Mineral Mapping Using Drill-Core Hyperspectral Image","display_name":"A New Approach for Mineral Mapping Using Drill-Core Hyperspectral Image","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4387987050","doi":"https://doi.org/10.1109/lgrs.2023.3328139"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2023.3328139","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3328139","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5065119339","display_name":"Lei Zhang","orcid":"https://orcid.org/0000-0002-2171-8674"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]},{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lei Zhang","raw_affiliation_strings":["Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China","School of Remote Sensing and Information Engineering, Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I4210118728","https://openalex.org/I37461747"]},{"raw_affiliation_string":"School of Remote Sensing and Information Engineering, Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I4210118728","https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100447212","display_name":"Ming Zhang","orcid":"https://orcid.org/0000-0001-5497-5071"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Zhang","raw_affiliation_strings":["School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan, China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111657935","display_name":"Jiejun Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiejun Huang","raw_affiliation_strings":["School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan, China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100380082","display_name":"Chuan Zhang","orcid":"https://orcid.org/0000-0002-7736-6487"},"institutions":[{"id":"https://openalex.org/I4210134214","display_name":"Beijing Research Institute of Uranium Geology","ror":"https://ror.org/046qx3a23","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210134214"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuan Zhang","raw_affiliation_strings":["National Key Laboratory of Remote Sensing Information and Imagery Analyzing Technology, Beijing Research Institute of Uranium Geology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Remote Sensing Information and Imagery Analyzing Technology, Beijing Research Institute of Uranium Geology, Beijing, China","institution_ids":["https://openalex.org/I4210134214"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052658014","display_name":"Fawang Ye","orcid":null},"institutions":[{"id":"https://openalex.org/I4210134214","display_name":"Beijing Research Institute of Uranium Geology","ror":"https://ror.org/046qx3a23","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210134214"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fawang Ye","raw_affiliation_strings":["National Key Laboratory of Remote Sensing Information and Imagery Analyzing Technology, Beijing Research Institute of Uranium Geology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Remote Sensing Information and Imagery Analyzing Technology, Beijing Research Institute of Uranium Geology, Beijing, China","institution_ids":["https://openalex.org/I4210134214"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100721259","display_name":"Wei Pan","orcid":"https://orcid.org/0000-0003-1121-9879"},"institutions":[{"id":"https://openalex.org/I4210134214","display_name":"Beijing Research Institute of Uranium Geology","ror":"https://ror.org/046qx3a23","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210134214"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Pan","raw_affiliation_strings":["National Key Laboratory of Remote Sensing Information and Imagery Analyzing Technology, Beijing Research Institute of Uranium Geology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Remote Sensing Information and Imagery Analyzing Technology, Beijing Research Institute of Uranium Geology, Beijing, China","institution_ids":["https://openalex.org/I4210134214"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5065119339"],"corresponding_institution_ids":["https://openalex.org/I37461747","https://openalex.org/I4210118728"],"apc_list":null,"apc_paid":null,"fwci":2.6111,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.91858736,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"20","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9987000226974487,"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/T12282","display_name":"Mineral Processing and Grinding","score":0.9846000075340271,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"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":0.9242069721221924},{"id":"https://openalex.org/keywords/drill","display_name":"Drill","score":0.6418416500091553},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6238229870796204},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5728539228439331},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4958365857601166},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4822962284088135},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.44061946868896484},{"id":"https://openalex.org/keywords/mineral-exploration","display_name":"Mineral exploration","score":0.4357214570045471},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4194268584251404},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.3454200327396393},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06954669952392578}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9242069721221924},{"id":"https://openalex.org/C173736775","wikidata":"https://www.wikidata.org/wiki/Q58964","display_name":"Drill","level":2,"score":0.6418416500091553},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6238229870796204},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5728539228439331},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4958365857601166},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4822962284088135},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.44061946868896484},{"id":"https://openalex.org/C66264921","wikidata":"https://www.wikidata.org/wiki/Q1370637","display_name":"Mineral exploration","level":2,"score":0.4357214570045471},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4194268584251404},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.3454200327396393},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06954669952392578},{"id":"https://openalex.org/C17409809","wikidata":"https://www.wikidata.org/wiki/Q161764","display_name":"Geochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2023.3328139","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3328139","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6800573563","display_name":null,"funder_award_id":"42201369","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":16,"referenced_works":["https://openalex.org/W1984710860","https://openalex.org/W1990033735","https://openalex.org/W1992111150","https://openalex.org/W2100921418","https://openalex.org/W2527344479","https://openalex.org/W2622741406","https://openalex.org/W2945254408","https://openalex.org/W2960241714","https://openalex.org/W2971007343","https://openalex.org/W2990457066","https://openalex.org/W3093896170","https://openalex.org/W3104418202","https://openalex.org/W3137075977","https://openalex.org/W3177932121","https://openalex.org/W3210458411","https://openalex.org/W4379054064"],"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/W2070598848","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2019190440","https://openalex.org/W2343470940"],"abstract_inverted_index":{"Hyperspectral":[0],"remote":[1],"sensing":[2],"technology":[3],"has":[4,14,36,79],"been":[5],"successfully":[6],"applied":[7],"to":[8,25,64,90,120],"geological":[9,191,234],"fields.":[10],"Drill-core":[11],"hyperspectral":[12,48,126,131],"imagery":[13],"the":[15,31,38,53,92,112,129,134,144,157,161,167,181,189,194,205,214,217],"characteristics":[16],"of":[17,57,114,136,146,160,170,186,198,216],"segmented":[18],"processing":[19],"and":[20,28,59,84,117,149,166,172,204],"large":[21],"data":[22,132],"volume.":[23],"Due":[24],"simple":[26],"principle":[27],"high":[29],"accuracy,":[30,95],"Spectral":[32],"Angle":[33,107],"Mapping":[34,108],"(SAM)":[35],"become":[37],"most":[39],"commonly":[40],"used":[41],"method":[42,219,224],"for":[43,229],"mineral":[44,69,93,122,182,199,230],"mapping":[45,94,151,163,174,183],"using":[46],"drill-core":[47,68,125,130],"images.":[49,127],"However,":[50],"SAM":[51,171],"analyzes":[52],"entire":[54],"spectral":[55,118],"form":[56],"minerals,":[58],"is":[60,164,202],"not":[61],"sensitive":[62],"enough":[63],"small":[65],"differences":[66],"in":[67,220,233],"spectra.":[70],"Compared":[71],"with":[72,188],"traditional":[73],"machine":[74],"learning":[75,78,83,116],"methods,":[76],"deep":[77,115],"more":[80],"powerful":[81],"feature":[82,85],"expression":[86],"capabilities.":[87],"In":[88],"order":[89],"improve":[91],"this":[96,141,221],"paper":[97,142],"proposes":[98],"a":[99,226],"new":[100,227],"approach":[101],"called":[102],"Graph":[103],"Convolutional":[104],"Neural":[105],"Networks-Spectral":[106],"(GCNNSAM),":[109],"which":[110,212],"integrates":[111],"advantages":[113],"matching":[119],"extract":[121],"information":[123,231],"from":[124],"Taking":[128],"near":[133],"depth":[135],"240m":[137],"as":[138],"an":[139],"example,":[140],"compares":[143],"performances":[145],"SAM,":[147],"GCNN":[148,173],"GCNNSAM":[150,162,187],"methods.":[152],"The":[153,223],"results":[154,185],"show":[155],"that":[156],"overall":[158,168],"accuracy":[159],"89.23%,":[165],"accuracies":[169],"methods":[175],"are":[176,207],"80.25%,":[177],"83.58%,":[178],"respectively.":[179],"Comparing":[180],"statistical":[184,192,196],"measured":[190],"results,":[193],"maximum":[195],"error":[197],"relative":[200],"content":[201],"1.4%,":[203],"errors":[206],"all":[208],"less":[209],"than":[210],"2%,":[211],"verifies":[213],"reliability":[215],"proposed":[218],"study.":[222],"provides":[225],"idea":[228],"acquisition":[232],"research.":[235]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-02T08:37:19.008085","created_date":"2025-10-10T00:00:00"}
