{"id":"https://openalex.org/W4414230220","doi":"https://doi.org/10.1109/tgrs.2025.3610348","title":"CMFNet: Cross Mamba Fusion Network for Hyperspectral and LiDAR Data Classification","display_name":"CMFNet: Cross Mamba Fusion Network for Hyperspectral and LiDAR Data Classification","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4414230220","doi":"https://doi.org/10.1109/tgrs.2025.3610348"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2025.3610348","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3610348","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","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/A5100610866","display_name":"Ziqi Li","orcid":"https://orcid.org/0000-0003-4694-4456"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ziqi Li","raw_affiliation_strings":["School of Automation, Wuxi University, Wuxi, China"],"affiliations":[{"raw_affiliation_string":"School of Automation, Wuxi University, Wuxi, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108264122","display_name":"Jiang Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I200845125","display_name":"Nanjing University of Information Science and Technology","ror":"https://ror.org/02y0rxk19","country_code":"CN","type":"education","lineage":["https://openalex.org/I200845125"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiang Wu","raw_affiliation_strings":["School of Automation and Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China","School of Automation, Nanjing University of Information Science &#x0026; Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Automation and Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I200845125"]},{"raw_affiliation_string":"School of Automation, Nanjing University of Information Science &#x0026; Technology, Nanjing, China","institution_ids":["https://openalex.org/I200845125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077369098","display_name":"Yonghong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yonghong Zhang","raw_affiliation_strings":["School of Automation, Wuxi University, Wuxi, China"],"affiliations":[{"raw_affiliation_string":"School of Automation, Wuxi University, Wuxi, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101299336","display_name":"Yu Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I200845125","display_name":"Nanjing University of Information Science and Technology","ror":"https://ror.org/02y0rxk19","country_code":"CN","type":"education","lineage":["https://openalex.org/I200845125"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Yan","raw_affiliation_strings":["School of Automation and Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China","School of Automation, Nanjing University of Information Science &#x0026; Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Automation and Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I200845125"]},{"raw_affiliation_string":"School of Automation, Nanjing University of Information Science &#x0026; Technology, Nanjing, China","institution_ids":["https://openalex.org/I200845125"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100610866"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0862,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.83489657,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"63","issue":null,"first_page":"1","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.8493000268936157,"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":0.8493000268936157,"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":0.7461000084877014},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.7271999716758728},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.7186999917030334},{"id":"https://openalex.org/keywords/data-redundancy","display_name":"Data redundancy","score":0.5716999769210815},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5579000115394592},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5551999807357788},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5375000238418579},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5317000150680542},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5041999816894531}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7871999740600586},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7461000084877014},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.7271999716758728},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.7186999917030334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6262000203132629},{"id":"https://openalex.org/C7545210","wikidata":"https://www.wikidata.org/wiki/Q838123","display_name":"Data redundancy","level":2,"score":0.5716999769210815},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5580000281333923},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5579000115394592},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5551999807357788},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5375000238418579},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5317000150680542},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5041999816894531},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4406999945640564},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.43130001425743103},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.36980000138282776},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.34700000286102295},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.33550000190734863},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3319999873638153},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.3301999866962433},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2870999872684479},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.27709999680519104},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.27149999141693115},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.26460000872612},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.2596000134944916}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2025.3610348","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3610348","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3256866207","display_name":null,"funder_award_id":"2021YFE0116900","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G5412665680","display_name":null,"funder_award_id":"42175157","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7331189401","display_name":null,"funder_award_id":"42475151","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/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W2039409148","https://openalex.org/W2765739551","https://openalex.org/W2793941577","https://openalex.org/W2808098982","https://openalex.org/W2886666371","https://openalex.org/W2890133123","https://openalex.org/W2987619624","https://openalex.org/W3004968762","https://openalex.org/W3008439211","https://openalex.org/W3012975023","https://openalex.org/W3016244469","https://openalex.org/W3035356612","https://openalex.org/W3047443805","https://openalex.org/W3048631361","https://openalex.org/W3080321686","https://openalex.org/W3122028341","https://openalex.org/W3135282456","https://openalex.org/W3196386002","https://openalex.org/W3202370946","https://openalex.org/W3208935369","https://openalex.org/W3209540366","https://openalex.org/W3214821343","https://openalex.org/W4210794570","https://openalex.org/W4285134520","https://openalex.org/W4312465065","https://openalex.org/W4315606133","https://openalex.org/W4319069095","https://openalex.org/W4361982595","https://openalex.org/W4379984088","https://openalex.org/W4380763457","https://openalex.org/W4386145024","https://openalex.org/W4388543795","https://openalex.org/W4389104771","https://openalex.org/W4390873437","https://openalex.org/W4391877704","https://openalex.org/W4391935886","https://openalex.org/W4392061677","https://openalex.org/W4392578770","https://openalex.org/W4395017283","https://openalex.org/W4399039901","https://openalex.org/W4399728165","https://openalex.org/W4399849901","https://openalex.org/W4400810737","https://openalex.org/W4402473688","https://openalex.org/W4403060848","https://openalex.org/W4403390894","https://openalex.org/W4404479756","https://openalex.org/W4404628516","https://openalex.org/W4405718036","https://openalex.org/W4406046828","https://openalex.org/W4406754142","https://openalex.org/W4406893126","https://openalex.org/W4408323584","https://openalex.org/W4408859628","https://openalex.org/W4409581361","https://openalex.org/W4410294717"],"related_works":[],"abstract_inverted_index":{"The":[0,82],"joint":[1],"classification":[2],"of":[3,47,60,123,185],"hyperspectral":[4,77],"image":[5],"(HSI)":[6],"and":[7,10,42,49,58,78,94,106,154],"light":[8],"detection":[9],"ranging":[11],"(LiDAR)":[12],"data,":[13,125],"which":[14],"leverages":[15],"their":[16],"complementary":[17,121],"information,":[18],"has":[19],"emerged":[20],"as":[21],"a":[22,70,86,110,128,160],"crucial":[23],"research":[24],"direction":[25],"in":[26,33,37,45],"remote":[27,168],"sensing.":[28],"However,":[29],"the":[30,120,136,183,189],"significant":[31],"differences":[32],"imaging":[34],"mechanisms":[35],"result":[36],"high":[38],"heterogeneity":[39],"between":[40],"HSI":[41],"LiDAR":[43,79],"data":[44,80],"terms":[46],"dimensionality":[48],"feature":[50,92,100,148,157],"distributions,":[51],"posing":[52],"substantial":[53],"challenges":[54],"for":[55,76],"semantic":[56],"representation":[57],"correlation":[59],"multimodal":[61,91],"data.":[62],"To":[63,117],"address":[64],"this":[65,67],"challenge,":[66],"article":[68],"proposes":[69],"Cross":[71,129],"Mamba":[72,130,137],"Fusion":[73,131],"Network":[74],"(CMFNet)":[75],"classification.":[81],"proposed":[83],"framework":[84],"adopts":[85],"dual-branch":[87],"architecture":[88,138],"to":[89,139],"facilitate":[90],"extraction":[93,158],"interaction,":[95],"initially":[96],"performing":[97],"multiscale":[98],"convolutional":[99],"embedding":[101],"before":[102],"adaptively":[103],"fusing":[104],"global":[105],"local":[107],"features":[108],"through":[109,159],"dedicated":[111],"Global-Local":[112],"Feature":[113],"Extraction":[114],"(GLFE)":[115],"module.":[116],"further":[118],"exploit":[119],"advantages":[122],"multi-source":[124],"we":[126],"design":[127],"(CMF)":[132],"module":[133,145],"that":[134,172],"extends":[135],"enable":[140],"efficient":[141],"dual-modal":[142],"interaction.":[143],"This":[144],"enhances":[146],"cross-modal":[147],"fusion":[149],"while":[150],"reducing":[151],"computational":[152],"redundancy":[153],"achieves":[155],"hierarchical":[156],"multi-layer":[161],"structure.":[162],"Comprehensive":[163],"experiments":[164],"on":[165,198],"three":[166],"benchmark":[167],"sensing":[169],"datasets":[170],"demonstrate":[171],"CMFNet":[173],"significantly":[174],"outperforms":[175],"state-of-the-art":[176],"methods,":[177],"with":[178],"ablation":[179],"studies":[180],"thoroughly":[181],"validating":[182],"effectiveness":[184],"each":[186],"component.":[187],"Habitually,":[188],"CMFNet\u2019s":[190],"source":[191],"code":[192],"can":[193],"be":[194],"made":[195],"publicly":[196],"accessible":[197],"my":[199],"profile":[200],"page":[201],"at":[202],"https://github.com/li-zi-qi/CMFNet.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
