{"id":"https://openalex.org/W7138395031","doi":"https://doi.org/10.1609/aaai.v40i12.37988","title":"UMNet: Uncertainty-guided Memory Network for Hyperspectral Pansharpening","display_name":"UMNet: Uncertainty-guided Memory Network for Hyperspectral Pansharpening","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138395031","doi":"https://doi.org/10.1609/aaai.v40i12.37988"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i12.37988","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i12.37988","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i12.37988","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125677143","display_name":"Xiaozheng Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tiangong University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaozheng Wang","raw_affiliation_strings":["Tiangong University"],"affiliations":[{"raw_affiliation_string":"Tiangong University","institution_ids":["https://openalex.org/I198091727"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129684449","display_name":"Yong Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tiangong University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Yang","raw_affiliation_strings":["Tiangong University"],"affiliations":[{"raw_affiliation_string":"Tiangong University","institution_ids":["https://openalex.org/I198091727"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129707527","display_name":"Shuying Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tiangong University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuying Huang","raw_affiliation_strings":["Tiangong University"],"affiliations":[{"raw_affiliation_string":"Tiangong University","institution_ids":["https://openalex.org/I198091727"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018985705","display_name":"Nayu Liu","orcid":"https://orcid.org/0000-0002-7664-9856"},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tiangong University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nayu Liu","raw_affiliation_strings":["Tiangong University"],"affiliations":[{"raw_affiliation_string":"Tiangong University","institution_ids":["https://openalex.org/I198091727"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5129716075","display_name":"Ziyang Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tiangong University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziyang Liu","raw_affiliation_strings":["Tiangong University"],"affiliations":[{"raw_affiliation_string":"Tiangong University","institution_ids":["https://openalex.org/I198091727"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5125677143"],"corresponding_institution_ids":["https://openalex.org/I198091727"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.70588235,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"12","first_page":"10199","last_page":"10206"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9876999855041504,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9876999855041504,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.008299999870359898,"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":0.0010000000474974513,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6646000146865845},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6261000037193298},{"id":"https://openalex.org/keywords/sharpening","display_name":"Sharpening","score":0.5317000150680542},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5037999749183655},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.4925999939441681},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.44699999690055847},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.41440001130104065},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.396699994802475},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.38659998774528503}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7409999966621399},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6794999837875366},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6646000146865845},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6261000037193298},{"id":"https://openalex.org/C2781137444","wikidata":"https://www.wikidata.org/wiki/Q237105","display_name":"Sharpening","level":2,"score":0.5317000150680542},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5037999749183655},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.4925999939441681},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.44699999690055847},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.41440001130104065},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.396699994802475},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.38659998774528503},{"id":"https://openalex.org/C150060386","wikidata":"https://www.wikidata.org/wiki/Q7574054","display_name":"Spatial correlation","level":2,"score":0.36399999260902405},{"id":"https://openalex.org/C17137986","wikidata":"https://www.wikidata.org/wiki/Q215067","display_name":"Orthogonality","level":2,"score":0.3409999907016754},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.33309999108314514},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.3253999948501587},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.26100000739097595},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2606000006198883},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C173414695","wikidata":"https://www.wikidata.org/wiki/Q5510276","display_name":"Fusion mechanism","level":4,"score":0.2572000026702881},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i12.37988","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i12.37988","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i12.37988","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i12.37988","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"At":[0],"present,":[1],"most":[2],"hyperspectral":[3],"(HS)":[4],"sharpening":[5],"methods":[6],"have":[7,21],"not":[8],"fully":[9],"utilized":[10],"the":[11,24,31,34,46,55,82,90,124,134,151,157,164,189],"feature":[12,27],"correlation":[13,91],"between":[14,92],"adjacent":[15,93],"bands":[16,94],"in":[17,49,54,133,196],"HS":[18,70,131,152],"images,":[19],"nor":[20],"they":[22],"explored":[23],"problem":[25],"of":[26,84,128],"uncertainty":[28,141],"generated":[29,44],"by":[30,45,160],"model":[32,158],"during":[33],"fusion":[35,42,56,75],"process.":[36],"This":[37],"may":[38],"lead":[39],"to":[40,95,122,155],"inaccurate":[41],"features":[43,100],"model,":[47],"resulting":[48],"spatial":[50,99,172,198],"and":[51,98,103,130,171,199],"spectral":[52,97,170,200],"distortions":[53],"results.":[57],"To":[58],"address":[59],"these":[60],"issues,":[61],"we":[62,143],"propose":[63],"an":[64],"uncertainty-guided":[65,147],"memory":[66,110],"network":[67,165],"(UMNet)":[68],"for":[69,150],"pansharpening.":[71],"A":[72],"spatial-spectral":[73,126,146],"recurrent":[74,135],"unit":[76,112],"(SRFU)":[77],"is":[78,114],"designed":[79],"based":[80,116,139],"on":[81,117,140,176],"concept":[83],"temporal":[85],"data":[86],"modeling,":[87],"which":[88],"utilizes":[89],"fuse":[96],"from":[101],"PAN":[102,129],"LRHS":[104],"images.":[105],"In":[106],"SRFU,":[107],"a":[108],"state":[109,136],"interaction":[111],"(SMIU)":[113],"constructed":[115],"non-negative":[118],"matrix":[119],"factorization":[120],"(NMF)":[121],"learn":[123],"global":[125],"dependency":[127],"images":[132],"space.":[137],"Moreover,":[138],"theory,":[142],"define":[144],"two":[145],"loss":[148],"functions":[149],"pansharpening":[153],"task":[154],"train":[156],"step":[159],"step,":[161],"ensuring":[162],"that":[163],"can":[166],"reconstruct":[167],"more":[168],"accurate":[169],"features.":[173],"Extensive":[174],"experiments":[175],"three":[177],"widely":[178],"used":[179],"datasets":[180],"demonstrate":[181],"that,":[182],"compared":[183],"with":[184],"some":[185],"state-of-the-art":[186],"(SOTA)":[187],"methods,":[188],"proposed":[190],"UMNet":[191],"has":[192],"achieved":[193],"significant":[194],"improvements":[195],"both":[197],"quality":[201],"metrics.":[202]},"counts_by_year":[],"updated_date":"2026-03-20T20:47:17.329874","created_date":"2026-03-18T00:00:00"}
