{"id":"https://openalex.org/W7154361349","doi":"https://doi.org/10.48550/arxiv.2604.09948","title":"Unmixing-Guided Spatial-Spectral Mamba with Clustering Tokens for Hyperspectral Image Classification","display_name":"Unmixing-Guided Spatial-Spectral Mamba with Clustering Tokens for Hyperspectral Image Classification","publication_year":2026,"publication_date":"2026-04-10","ids":{"openalex":"https://openalex.org/W7154361349","doi":"https://doi.org/10.48550/arxiv.2604.09948"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.09948","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09948","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.09948","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133574739","display_name":"Yimin Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yimin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133558384","display_name":"Lincoln Linlin Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Lincoln Linlin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.984499990940094,"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":0.984499990940094,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.0032999999821186066,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.001500000013038516,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6952999830245972},{"id":"https://openalex.org/keywords/endmember","display_name":"Endmember","score":0.692799985408783},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5834000110626221},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.5299000144004822},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5278000235557556},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.48649999499320984},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4291999936103821},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.38019999861717224},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.37470000982284546}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6952999830245972},{"id":"https://openalex.org/C58237817","wikidata":"https://www.wikidata.org/wiki/Q5376204","display_name":"Endmember","level":3,"score":0.692799985408783},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6812999844551086},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6751999855041504},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5834000110626221},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5299000144004822},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5278000235557556},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.48649999499320984},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4291999936103821},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.38019999861717224},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.37470000982284546},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3580000102519989},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.34790000319480896},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.33559998869895935},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3312000036239624},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3206999897956848},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3140000104904175},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2976999878883362},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2581000030040741},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.2538999915122986},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.09948","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09948","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.09948","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09948","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.4302878677845001,"display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Although":[0],"hyperspectral":[1],"image":[2],"(HSI)":[3],"classification":[4,165,186],"is":[5,13,170,214],"critical":[6],"for":[7,45,62,87,117,172],"supporting":[8],"various":[9],"environmental":[10],"applications,":[11],"it":[12],"a":[14,37,68,138,167,177,190],"challenging":[15],"task":[16],"due":[17],"to":[18,28,54,91,112,122,158,176],"the":[19,22,26,50,56,98,115,132,161,209],"spectral-mixture":[20],"effect,":[21],"spatial-spectral":[23,40,124,141],"heterogeneity":[24],"and":[25,32,79,127,155,164,193],"difficulty":[27],"preserve":[29],"class":[30],"boundaries":[31],"details.":[33],"This":[34],"letter":[35],"presents":[36],"novel":[38,69,139],"unmixing-guided":[39,140],"Mamba":[41,93,142,148],"with":[42,49],"clustering":[43],"tokens":[44,116],"improved":[46,63,118],"HSI":[47,61,83,200],"classification,":[48],"following":[51],"contributions.":[52],"First,":[53],"disentangle":[55],"spectral":[57,70],"mixture":[58],"effect":[59],"in":[60,150],"pattern":[64],"discovery,":[65],"we":[66,104,136],"design":[67,105,137],"unmixing":[71],"network":[72],"that":[73,144,181,203],"not":[74,183],"only":[75,184],"automatically":[76],"learns":[77],"endmembers":[78],"abundance":[80,102,194],"maps":[81,187],"from":[82],"but":[84,188],"also":[85,189],"accounts":[86],"endmember":[88],"variabilities.":[89],"Second,":[90],"generate":[92],"token":[94,109,134,153],"sequences,":[95,135],"based":[96,130],"on":[97,131,198],"clusters":[99],"defined":[100],"by":[101],"maps,":[103],"an":[106],"efficient":[107],"Top-\\textit{K}":[108,133],"selection":[110],"strategy":[111],"adaptively":[113],"sequence":[114],"representational":[119],"capability.":[120],"Third,":[121],"improve":[123],"feature":[125],"learning":[126,154],"detail":[128],"preservation,":[129],"module":[143],"greatly":[145,207],"improves":[146],"traditional":[147],"models":[149],"terms":[151],"of":[152],"sequencing.":[156],"Fourth,":[157],"learn":[159],"simultaneously":[160],"endmember-abundance":[162],"patterns":[163],"labels,":[166],"multi-task":[168],"scheme":[169],"designed":[171],"model":[173,205],"supervision,":[174],"leading":[175],"new":[178],"unmixing-classification":[179],"framework":[180],"outputs":[182],"accurate":[185],"comprehensive":[191],"spectral-library":[192],"maps.":[195],"Comparative":[196],"experiments":[197],"four":[199],"datasets":[201],"demonstrate":[202],"our":[204],"can":[206],"outperform":[208],"other":[210],"state-of-the-art":[211],"approaches.":[212],"Code":[213],"available":[215],"at":[216],"https://github.com/GSIL-UCalgary/Unmixing_guided_Mamba.git":[217]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-15T00:00:00"}