{"id":"https://openalex.org/W3130065951","doi":"https://doi.org/10.1109/igarss39084.2020.9323795","title":"Unbalanced Geologic Body Classification of Hyperspectral Data Based on Squeeze and Excitation Networks at Tianshan Area","display_name":"Unbalanced Geologic Body Classification of Hyperspectral Data Based on Squeeze and Excitation Networks at Tianshan Area","publication_year":2020,"publication_date":"2020-09-26","ids":{"openalex":"https://openalex.org/W3130065951","doi":"https://doi.org/10.1109/igarss39084.2020.9323795","mag":"3130065951"},"language":"en","primary_location":{"id":"doi:10.1109/igarss39084.2020.9323795","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323795","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-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/A5025997006","display_name":"Yuchen Liang","orcid":"https://orcid.org/0000-0002-6433-9164"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuchen Liang","raw_affiliation_strings":["School of artificial intelligence, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of artificial intelligence, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103247632","display_name":"Zhengang Zhao","orcid":"https://orcid.org/0000-0003-1482-5276"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengang Zhao","raw_affiliation_strings":["School of artificial intelligence, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of artificial intelligence, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100743778","display_name":"Hao Wang","orcid":"https://orcid.org/0000-0002-3964-479X"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Wang","raw_affiliation_strings":["School of artificial intelligence, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of artificial intelligence, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102759361","display_name":"Ying Cao","orcid":"https://orcid.org/0000-0002-8256-8497"},"institutions":[{"id":"https://openalex.org/I4210091612","display_name":"Beijing Institute of Geology for Mineral Resources","ror":"https://ror.org/00cq3qn16","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210091612"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Cao","raw_affiliation_strings":["Beijing Institute of Geology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Geology, Beijing, China","institution_ids":["https://openalex.org/I4210091612"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100767738","display_name":"Tao Huang","orcid":"https://orcid.org/0000-0002-8098-8906"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Huang","raw_affiliation_strings":["School of artificial intelligence, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of artificial intelligence, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014659259","display_name":"Yasmine Medjadba","orcid":null},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yasmine Medjadba","raw_affiliation_strings":["School of artificial intelligence, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of artificial intelligence, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100605899","display_name":"Yuntao Wang","orcid":"https://orcid.org/0000-0001-6232-9723"},"institutions":[{"id":"https://openalex.org/I4210091612","display_name":"Beijing Institute of Geology for Mineral Resources","ror":"https://ror.org/00cq3qn16","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210091612"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuntao Wang","raw_affiliation_strings":["Beijing Institute of Geology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Geology, Beijing, China","institution_ids":["https://openalex.org/I4210091612"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031321682","display_name":"Run Cheng Jiao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210091612","display_name":"Beijing Institute of Geology for Mineral Resources","ror":"https://ror.org/00cq3qn16","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210091612"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Run Cheng Jiao","raw_affiliation_strings":["Beijing Institute of Geology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Geology, Beijing, China","institution_ids":["https://openalex.org/I4210091612"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100762807","display_name":"Siying Chen","orcid":"https://orcid.org/0000-0003-1565-5125"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siying Chen","raw_affiliation_strings":["School of artificial intelligence, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of artificial intelligence, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101778276","display_name":"Xianchuan Yu","orcid":"https://orcid.org/0000-0002-1425-0751"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianchuan Yu","raw_affiliation_strings":["School of artificial intelligence, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of artificial intelligence, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5025997006"],"corresponding_institution_ids":["https://openalex.org/I25254941"],"apc_list":null,"apc_paid":null,"fwci":0.3773,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.69840962,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"6981","last_page":"6984"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9986000061035156,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9677000045776367,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8972116708755493},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6829678416252136},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6735106706619263},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6190997958183289},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5749824047088623},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5643477439880371},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5563684701919556},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5245987176895142},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4714905917644501},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4107191264629364},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.38043949007987976},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16714724898338318},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11954769492149353}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8972116708755493},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6829678416252136},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6735106706619263},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6190997958183289},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5749824047088623},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5643477439880371},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5563684701919556},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5245987176895142},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4714905917644501},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4107191264629364},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.38043949007987976},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16714724898338318},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11954769492149353},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss39084.2020.9323795","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323795","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"No poverty","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/1"}],"awards":[{"id":"https://openalex.org/G3998186678","display_name":null,"funder_award_id":"L172029","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G5176970212","display_name":null,"funder_award_id":"41672323","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"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W2043665634","https://openalex.org/W2092071303","https://openalex.org/W2108725536","https://openalex.org/W2752782242","https://openalex.org/W2889861425","https://openalex.org/W2963351448","https://openalex.org/W6600284362"],"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/W2044184146","https://openalex.org/W2070598848","https://openalex.org/W4313014865","https://openalex.org/W2964954556"],"abstract_inverted_index":{"Hyperspectral":[0],"data":[1,122],"contains":[2],"abundant":[3],"spectral":[4],"do":[5],"main":[6],"information,":[7],"which":[8,83,145],"is":[9,26,72],"of":[10,15,22,39,66,109],"great":[11,138],"significance":[12],"to":[13,19,28,58,105],"classification":[14,46,143],"objects.":[16],"However,":[17],"due":[18],"the":[20,36,55,60,64,67,75,80,87,94,98,107,127,141],"lack":[21],"labeled":[23,40],"data,":[24],"it":[25],"difficult":[27],"get":[29,136],"an":[30],"acceptable":[31],"result":[32,139],"by":[33,91,113],"just":[34],"using":[35],"small":[37],"number":[38],"data.":[41],"We":[42,116],"propose":[43],"a":[44,137],"semi-supervised":[45],"model":[47,119],"based":[48],"on":[49,74,120,140],"convolutional":[50],"neural":[51],"network":[52],"and":[53],"introduce":[54],"attention":[56],"mechanism":[57],"balance":[59],"sample":[61],"weight.":[62],"After":[63],"convolution":[65],"multi-layer":[68],"network,":[69],"more":[70],"information":[71],"concentrated":[73],"channels,":[76],"so":[77],"we":[78,101,129],"use":[79],"Squeeze-and-Excitation":[81],"block,":[82],"can":[84,130,135,146],"adaptively":[85],"recalibrates":[86],"channel-wise":[88],"characteristic":[89],"response":[90],"explicitly":[92],"modelling":[93],"inter-channel":[95],"dependencies.":[96],"At":[97],"same":[99],"time,":[100],"used":[102,148],"focal":[103],"loss":[104],"reduce":[106],"problem":[108],"poor":[110],"training":[111],"caused":[112],"uneven":[114],"samples.":[115],"test":[117],"our":[118,133],"hyperspectral":[121],"at":[123],"Tianshan":[124],"area.":[125],"From":[126],"result,":[128],"find":[131],"that":[132],"method":[134],"mineral":[142],"task,":[144],"be":[147],"for":[149],"making":[150],"geological":[151],"map.":[152]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}