{"id":"https://openalex.org/W4312660916","doi":"https://doi.org/10.1109/igarss46834.2022.9884892","title":"Cross-Layer Multi-Attention Guided Spectral-Spatial Classification of Hyperspectral Images","display_name":"Cross-Layer Multi-Attention Guided Spectral-Spatial Classification of Hyperspectral Images","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312660916","doi":"https://doi.org/10.1109/igarss46834.2022.9884892"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9884892","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884892","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 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/A5001186471","display_name":"Mengkai Liu","orcid":"https://orcid.org/0000-0001-9442-8216"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mengkai Liu","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University,Changsha,China","College of Electrical and Information Engineering, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University,Changsha,China","institution_ids":["https://openalex.org/I16609230"]},{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012967504","display_name":"Wei Fu","orcid":"https://orcid.org/0000-0002-8502-7696"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Fu","raw_affiliation_strings":["College of Computer Science and Electronic Engineering, Hunan University,Changsha,China","College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Electronic Engineering, Hunan University,Changsha,China","institution_ids":["https://openalex.org/I16609230"]},{"raw_affiliation_string":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102716602","display_name":"Ting Lu","orcid":"https://orcid.org/0000-0001-8415-0185"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Lu","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University,Changsha,China","College of Electrical and Information Engineering, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University,Changsha,China","institution_ids":["https://openalex.org/I16609230"]},{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5001186471"],"corresponding_institution_ids":["https://openalex.org/I16609230"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27064862,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3131","last_page":"3134"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9882000088691711,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9710000157356262,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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":0.876105785369873},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8365204334259033},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7755532264709473},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6905170679092407},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6517974138259888},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6351746916770935},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.632563054561615},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.5445341467857361},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4704628884792328},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4184334874153137},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4175671935081482},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.41698744893074036},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.36997729539871216},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.17217198014259338}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.876105785369873},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8365204334259033},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7755532264709473},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6905170679092407},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6517974138259888},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6351746916770935},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.632563054561615},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.5445341467857361},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4704628884792328},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4184334874153137},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4175671935081482},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.41698744893074036},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.36997729539871216},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.17217198014259338},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss46834.2022.9884892","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884892","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.75}],"awards":[{"id":"https://openalex.org/G1289097004","display_name":null,"funder_award_id":"62001160","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":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1965309615","https://openalex.org/W2152057649","https://openalex.org/W2764276316","https://openalex.org/W2884585870","https://openalex.org/W2942454403","https://openalex.org/W3000554425","https://openalex.org/W3002674187","https://openalex.org/W3031696400","https://openalex.org/W3114720220","https://openalex.org/W3170347305","https://openalex.org/W6753412334"],"related_works":["https://openalex.org/W2761785940","https://openalex.org/W2952813363","https://openalex.org/W4360783045","https://openalex.org/W2129933262","https://openalex.org/W2963346891","https://openalex.org/W3176438653","https://openalex.org/W2770149305","https://openalex.org/W3167930666","https://openalex.org/W3014952856","https://openalex.org/W3010730661"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"based":[2],"methods":[3,13],"are":[4,80,92,107],"very":[5],"popular":[6],"for":[7,47],"hyperspectral":[8],"image":[9],"classification.":[10],"However,":[11],"those":[12],"usually":[14],"ignore":[15],"the":[16,35,96,123,126],"fact":[17],"that":[18],"discriminative":[19],"information":[20,75],"lies":[21],"on":[22],"specific":[23],"spatial":[24,66,90,105],"positions":[25],"and":[26,38,65,89,104,109],"spectral":[27,64,88,103],"bands.":[28],"To":[29],"solve":[30],"this":[31],"problem,":[32],"we":[33],"introduce":[34],"attention":[36,74],"mechanism,":[37],"propose":[39],"a":[40,50,55,86,116],"cross-layer":[41,69],"multi-attention":[42,70],"guided":[43],"classification":[44],"network":[45,97],"(CLMA-Net)":[46],"HSIs.":[48],"First,":[49],"backbone":[51],"network,":[52,59],"which":[53,72],"is":[54,60],"two-branch":[56],"convolutional":[57,78],"neural":[58],"developed":[61],"to":[62,99,111],"extract":[63],"features.":[67],"Then,":[68],"modules,":[71],"integrate":[73],"of":[76,125],"multiple":[77],"layers,":[79],"embedded":[81],"into":[82],"two":[83],"branches.":[84],"As":[85],"result,":[87],"features":[91,106],"optimized":[93],"by":[94,115],"making":[95],"attend":[98],"interested":[100],"parts.":[101],"Finally,":[102],"concatenated":[108],"used":[110],"predict":[112],"class":[113],"label":[114],"fully":[117],"connected":[118],"layer.":[119],"Experimental":[120],"results":[121],"demonstrate":[122],"effectiveness":[124],"proposed":[127],"method.":[128],"The":[129],"code":[130],"will":[131],"be":[132],"available":[133],"at":[134],"https://github.com/mengkai-liu/CLMA-Net.":[135]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
