{"id":"https://openalex.org/W4312307335","doi":"https://doi.org/10.1109/jstars.2022.3213865","title":"CMR-CNN: Cross-Mixing Residual Network for Hyperspectral Image Classification","display_name":"CMR-CNN: Cross-Mixing Residual Network for Hyperspectral Image Classification","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4312307335","doi":"https://doi.org/10.1109/jstars.2022.3213865"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2022.3213865","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2022.3213865","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/4609444/09917315.pdf","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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://ieeexplore.ieee.org/ielx7/4609443/4609444/09917315.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078706989","display_name":"Zhen Yang","orcid":"https://orcid.org/0000-0002-5205-3281"},"institutions":[{"id":"https://openalex.org/I41317344","display_name":"Jiangxi Science and Technology Normal University","ror":"https://ror.org/04r1zkp10","country_code":"CN","type":"education","lineage":["https://openalex.org/I41317344"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Yang","raw_affiliation_strings":["School of Communication and Electronics, Jiangxi Science and Technology Normal University, Nanchang, China","Guangdong Atv Academy For Performing Arts, Dongguan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Communication and Electronics, Jiangxi Science and Technology Normal University, Nanchang, China","institution_ids":["https://openalex.org/I41317344"]},{"raw_affiliation_string":"Guangdong Atv Academy For Performing Arts, Dongguan, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101613444","display_name":"Zhipeng Xi","orcid":"https://orcid.org/0000-0003-2021-4842"},"institutions":[{"id":"https://openalex.org/I41317344","display_name":"Jiangxi Science and Technology Normal University","ror":"https://ror.org/04r1zkp10","country_code":"CN","type":"education","lineage":["https://openalex.org/I41317344"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhipeng Xi","raw_affiliation_strings":["School of Communication and Electronics, Jiangxi Science and Technology Normal University, Nanchang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Communication and Electronics, Jiangxi Science and Technology Normal University, Nanchang, China","institution_ids":["https://openalex.org/I41317344"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100375832","display_name":"Tao Zhang","orcid":"https://orcid.org/0000-0002-7192-5153"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Zhang","raw_affiliation_strings":["Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-7192-5153","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027639981","display_name":"Weiwei Guo","orcid":"https://orcid.org/0000-0001-5037-0972"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwei Guo","raw_affiliation_strings":["Center for Digital Innovation, Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-5037-0972","affiliations":[{"raw_affiliation_string":"Center for Digital Innovation, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059094652","display_name":"Zenghui Zhang","orcid":"https://orcid.org/0000-0002-1238-8538"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zenghui Zhang","raw_affiliation_strings":["Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-1238-8538","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015155189","display_name":"Heng-Chao Li","orcid":"https://orcid.org/0000-0002-9735-570X"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heng-Chao Li","raw_affiliation_strings":["School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China","National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-9735-570X","affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]},{"raw_affiliation_string":"National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":{"value":1250,"currency":"USD","value_usd":1250},"fwci":2.7434,"has_fulltext":true,"cited_by_count":27,"citation_normalized_percentile":{"value":0.91425779,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"15","issue":null,"first_page":"8974","last_page":"8989"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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":1.0,"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":0.9912999868392944,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9634000062942505,"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":0.8269601464271545},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7987110614776611},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7758299112319946},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7662767171859741},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7090328931808472},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6607480645179749},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6158295273780823},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4665954113006592},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44890138506889343},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4345552623271942},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.09738972783088684}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8269601464271545},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7987110614776611},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7758299112319946},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7662767171859741},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7090328931808472},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6607480645179749},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6158295273780823},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4665954113006592},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44890138506889343},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4345552623271942},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.09738972783088684},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jstars.2022.3213865","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2022.3213865","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/4609444/09917315.pdf","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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:1975796509da4f3daeae4ed49b20e243","is_oa":true,"landing_page_url":"https://doaj.org/article/1975796509da4f3daeae4ed49b20e243","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 8974-8989 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/jstars.2022.3213865","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2022.3213865","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/4609444/09917315.pdf","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":[{"id":"https://openalex.org/G5747709536","display_name":null,"funder_award_id":"62201343","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6141434176","display_name":null,"funder_award_id":"62271311","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7224411499","display_name":null,"funder_award_id":"62261026","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7509836859","display_name":null,"funder_award_id":"62271418","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8681329955","display_name":"\u9762\u5411SAR\u56fe\u50cf\u76ee\u6807\u8bc6\u522b\u7684\u6df1\u5ea6\u5b66\u4e60\u53ef\u89e3\u91ca\u6027\u7814\u7a76","funder_award_id":"62071333","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":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4312307335.pdf","grobid_xml":"https://content.openalex.org/works/W4312307335.grobid-xml"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W1966580635","https://openalex.org/W1982427174","https://openalex.org/W2022470997","https://openalex.org/W2069231830","https://openalex.org/W2087263574","https://openalex.org/W2101711129","https://openalex.org/W2104269704","https://openalex.org/W2136251662","https://openalex.org/W2194775991","https://openalex.org/W2500751094","https://openalex.org/W2532852010","https://openalex.org/W2546942002","https://openalex.org/W2572303978","https://openalex.org/W2587782381","https://openalex.org/W2614256707","https://openalex.org/W2626256547","https://openalex.org/W2736279234","https://openalex.org/W2754507318","https://openalex.org/W2755322760","https://openalex.org/W2761339476","https://openalex.org/W2764276316","https://openalex.org/W2791006446","https://openalex.org/W2793941577","https://openalex.org/W2809635958","https://openalex.org/W2887626628","https://openalex.org/W2888119354","https://openalex.org/W2907100627","https://openalex.org/W2914331134","https://openalex.org/W2919115771","https://openalex.org/W2928260953","https://openalex.org/W2950185713","https://openalex.org/W2991286101","https://openalex.org/W3005359536","https://openalex.org/W3031015423","https://openalex.org/W3046476394","https://openalex.org/W3066454894","https://openalex.org/W3093896170","https://openalex.org/W3103753223","https://openalex.org/W3104418202","https://openalex.org/W3128999341","https://openalex.org/W3194077607","https://openalex.org/W3199303234","https://openalex.org/W3207005322","https://openalex.org/W3209395768","https://openalex.org/W4210794570","https://openalex.org/W4226443111","https://openalex.org/W6926114150"],"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":{"With":[0,116],"the":[1,17,36,85,96,109,136,167],"development":[2],"of":[3,24,128,138,141],"deep":[4],"learning,":[5],"various":[6],"convolutional":[7],"neural":[8],"network":[9,70],"(CNN)":[10],"based":[11],"methods":[12,172],"have":[13],"been":[14],"proposed":[15,169],"for":[16,61,83,94,107],"hyperspectral":[18],"image":[19],"(HSI)":[20],"classification.":[21,63],"Although":[22],"most":[23],"them":[25],"achieve":[26,153],"good":[27],"classification":[28,171],"performance,":[29],"there":[30],"are":[31,113],"still":[32],"more":[33],"misclassifications":[34],"in":[35,135],"prediction":[37],"map":[38],"with":[39,166],"fewer":[40],"training":[41,142],"samples.":[42],"In":[43],"order":[44],"to":[45,52,118,147],"address":[46],"this":[47,49],"shortcoming,":[48],"paper":[50],"proposes":[51],"simultaneously":[53],"use":[54],"pixels'":[55],"spatial":[56,97],"information":[57,60],"and":[58,99,133,161,179,184],"spectral":[59,86],"HSI":[62,170],"Briefly":[64],"speaking,":[65],"a":[66],"new":[67],"cross-mixing":[68],"residual":[69,80,91],"denoted":[71],"by":[72,181],"CMR-CNN":[73,151,174],"is":[74],"developed,":[75],"wherein":[76],"one":[77,88,100],"three-dimensional":[78],"(3D)":[79],"structure":[81,92,105],"responsible":[82,93,106],"extracting":[84,95],"characteristics,":[87,98],"two-dimensional":[89],"(2D)":[90],"assisted":[101],"feature":[102],"extraction":[103],"(AFE)":[104],"linking":[108],"first":[110],"two":[111],"structures":[112],"respectively":[114,175],"designed.":[115],"respect":[117],"experiments":[119],"performed":[120],"on":[121,186],"five":[122],"different":[123,139],"datasets":[124],"Indian":[125],"Pines,":[126],"University":[127],"Pavia,":[129],"Salinas":[130],"Scene,":[131],"KSC,":[132],"Xuzhou":[134],"case":[137],"numbers":[140],"samples":[143],"show":[144],"that,":[145],"compared":[146,165],"some":[148],"state-of-the-art":[149],"methods,":[150],"can":[152],"higher":[154],"overall":[155],"accuracy":[156,159],"(OA),":[157],"average":[158],"(AA),":[160],"Kappa":[162],"values.":[163],"Particularly,":[164],"newly":[168],"OCT-MCNN,":[173],"improves":[176],"OA,":[177],"AA":[178],"kappa":[180],"4.13%,":[182],"3.67%,":[183],"2.75%":[185],"average.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":7}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
