{"id":"https://openalex.org/W7161705089","doi":"https://doi.org/10.48550/arxiv.2605.18541","title":"LESSViT: Robust Hyperspectral Representation Learning under Spectral Configuration Shift","display_name":"LESSViT: Robust Hyperspectral Representation Learning under Spectral Configuration Shift","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161705089","doi":"https://doi.org/10.48550/arxiv.2605.18541"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.18541","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18541","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.2605.18541","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136457795","display_name":"Haozhe Si","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Si, Haozhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136467379","display_name":"Yuxuan Wan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wan, Yuxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136505022","display_name":"Yuqing Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yuqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136466270","display_name":"Minh Do","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Do, Minh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136470062","display_name":"Han Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Han","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":0.9251000285148621,"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":0.9251000285148621,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.008200000040233135,"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/T11094","display_name":"Face Recognition and Perception","score":0.007000000216066837,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8442000150680542},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5805000066757202},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5084999799728394},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.46619999408721924},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42820000648498535},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.40290001034736633},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.38280001282691956},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.35989999771118164},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.35659998655319214}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8442000150680542},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5805000066757202},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5627999901771545},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5084999799728394},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4948999881744385},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.46619999408721924},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42820000648498535},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4072999954223633},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.40290001034736633},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.38280001282691956},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.35659998655319214},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3517000079154968},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C70710897","wikidata":"https://www.wikidata.org/wiki/Q680081","display_name":"Separable space","level":2,"score":0.31610000133514404},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2930999994277954},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C114700698","wikidata":"https://www.wikidata.org/wiki/Q2882278","display_name":"Spectral bands","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C176641082","wikidata":"https://www.wikidata.org/wiki/Q2446767","display_name":"Spectral signature","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.26159998774528503}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.18541","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18541","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.2605.18541","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18541","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Modeling":[0],"hyperspectral":[1,169,222],"imagery":[2],"(HSI)":[3],"across":[4],"different":[5],"sensors":[6],"presents":[7],"a":[8,23,28,65,82,94,168,185],"fundamental":[9,66],"challenge":[10],"due":[11],"to":[12,34,36,64,119,155],"variations":[13],"in":[14],"wavelength":[15],"coverage,":[16],"band":[17],"sampling,":[18],"and":[19,70,106,136,151,163,177,210,212,220],"channel":[20,52,179],"dimensionality.":[21],"As":[22],"result,":[24],"models":[25,99],"trained":[26],"under":[27,184,203],"fixed":[29,51],"spectral":[30,48,107,134,158,204],"configuration":[31],"often":[32],"fail":[33],"generalize":[35],"other":[37],"sensors.":[38],"Existing":[39],"Vision":[40],"Transformer":[41],"(ViT)":[42],"approaches":[43],"either":[44],"rely":[45],"on":[46,91,194],"implicit":[47],"modeling":[49,215],"with":[50,59,173],"assumptions":[53],"or":[54],"adopt":[55],"explicit":[56,211],"spatial-spectral":[57,101,114,175,214],"attention":[58,115],"prohibitive":[60],"computational":[61],"cost,":[62],"leading":[63],"trade-off":[67],"between":[68],"efficiency":[69],"expressiveness.":[71],"In":[72],"this":[73],"work,":[74],"we":[75,166],"introduce":[76,167],"Low-rank":[77],"Efficient":[78],"Spatial-Spectral":[79],"ViT":[80],"(LESSViT),":[81],"sensor-flexible":[83],"architecture":[84],"for":[85,218],"cross-spectral":[86,186],"generalization.":[87],"LESSViT":[88,183,200],"is":[89,123,130,138,216],"built":[90],"LESS":[92],"Attention,":[93],"structured":[95],"low-rank":[96,143],"factorization":[97],"that":[98,189,199],"joint":[100],"interactions":[102],"through":[103],"separable":[104],"spatial":[105,127],"components,":[108],"reducing":[109],"the":[110,124,131,139,142,195],"complexity":[111],"of":[112,126,133,141],"full":[113],"from":[116],"$O(N^2":[117],"C^2)$":[118],"$O(rNC)$,":[120],"where":[121],"$N$":[122],"number":[125,132],"tokens,":[128],"$C$":[129],"channels,":[135],"$r$":[137],"rank":[140],"approximation.":[144],"We":[145,181],"further":[146],"incorporate":[147],"channel-agnostic":[148],"patch":[149],"embedding":[150],"wavelength-aware":[152],"positional":[153],"encoding":[154],"support":[156],"flexible":[157],"inputs.":[159],"To":[160],"enable":[161],"efficient":[162,213],"robust":[164],"pretraining,":[165],"masked":[170],"autoencoder":[171],"(HyperMAE)":[172],"decoupled":[174],"masking":[176],"hierarchical":[178],"sampling.":[180],"evaluate":[182],"generalization":[187],"setting":[188],"simulates":[190],"cross-sensor":[191],"variability.":[192],"Experiments":[193],"SpectralEarth":[196],"benchmark":[197],"demonstrate":[198],"improves":[201],"robustness":[202],"shifts":[205],"while":[206],"remaining":[207],"competitive":[208],"in-distribution,":[209],"essential":[217],"scalable":[219],"generalizable":[221],"representation":[223],"learning.":[224]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-20T00:00:00"}