{"id":"https://openalex.org/W7121323104","doi":"https://doi.org/10.6084/m9.figshare.31042038","title":"HDP-Net: a hyperspectral deterministic-probabilistic dual attention network for hyperspectral image classification","display_name":"HDP-Net: a hyperspectral deterministic-probabilistic dual attention network for hyperspectral image classification","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7121323104","doi":"https://doi.org/10.6084/m9.figshare.31042038"},"language":"en","primary_location":{"id":"doi:10.6084/m9.figshare.31042038","doi":"https://doi.org/10.6084/m9.figshare.31042038","is_oa":true,"landing_page_url":"https://doi.org/10.6084/m9.figshare.31042038","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"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":"JournalArticle"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.6084/m9.figshare.31042038","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007111950","display_name":"Jianshang Liao","orcid":"https://orcid.org/0000-0003-0358-6089","observed_orcids":["https://orcid.org/0000-0003-0358-6089"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liao, Jianshang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5122737190","display_name":"Liguo Wang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Liguo","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01582198,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-image-classification","display_name":"hyperspectral image classification","score":1.0},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"uncertainty quantification","score":0.7599999904632568},{"id":"https://openalex.org/keywords/attention-mechanism","display_name":"attention mechanism","score":0.5730000138282776},{"id":"https://openalex.org/keywords/mixed-pixels","display_name":"mixed pixels","score":0.8889999985694885},{"id":"https://openalex.org/keywords/spectral-confusion","display_name":"spectral confusion","score":0.9729999899864197},{"id":"https://openalex.org/keywords/adaptive-feature-fusion","display_name":"adaptive feature fusion","score":0.9409999847412109},{"id":"https://openalex.org/keywords/confidence-estimation","display_name":"confidence estimation","score":0.7009999752044678},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"remote sensing","score":0.5139999985694885}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8086000084877014},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.7081999778747559},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6424999833106995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6399999856948853},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.637499988079071},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6008999943733215},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5461999773979187},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5108000040054321},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.505299985408783},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4320000112056732},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42260000109672546},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4221999943256378},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40380001068115234},{"id":"https://openalex.org/C81388566","wikidata":"https://www.wikidata.org/wiki/Q526668","display_name":"Sigmoid function","level":3,"score":0.39089998602867126},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3707999885082245},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.3492000102996826},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3303000032901764},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3278999924659729},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C138602881","wikidata":"https://www.wikidata.org/wiki/Q2709591","display_name":"Confusion matrix","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2825999855995178},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C167651023","wikidata":"https://www.wikidata.org/wiki/Q1474611","display_name":"Plot (graphics)","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.25999999046325684},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.2599000036716461},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.2524999976158142},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.25}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.6084/m9.figshare.31042038","doi":"https://doi.org/10.6084/m9.figshare.31042038","is_oa":true,"landing_page_url":"https://doi.org/10.6084/m9.figshare.31042038","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"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":"JournalArticle"},{"id":"doi:10.6084/m9.figshare.31042038.v1","doi":"https://doi.org/10.6084/m9.figshare.31042038.v1","is_oa":true,"landing_page_url":"https://doi.org/10.6084/m9.figshare.31042038.v1","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"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":"JournalArticle"}],"best_oa_location":{"id":"doi:10.6084/m9.figshare.31042038","doi":"https://doi.org/10.6084/m9.figshare.31042038","is_oa":true,"landing_page_url":"https://doi.org/10.6084/m9.figshare.31042038","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"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":"JournalArticle"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4413318180","https://openalex.org/W4414125552","https://openalex.org/W4384382770","https://openalex.org/W4403826623","https://openalex.org/W4382700610","https://openalex.org/W4399939472","https://openalex.org/W7126198810","https://openalex.org/W4405284487","https://openalex.org/W4391923792","https://openalex.org/W4283768648"],"abstract_inverted_index":{"Hyperspectral":[0,33],"image":[1],"classification":[2,201,233,271],"faces":[3],"significant":[4],"challenges":[5],"in":[6,19,150,197,277],"the":[7,98,104],"simultaneous":[8],"processing":[9],"of":[10,178,186,220],"deterministic":[11,49,89,112],"and":[12,25,72,114,154,181,183,189,217,247,273],"uncertain":[13],"features,":[14],"leading":[15],"to":[16,264],"suboptimal":[17],"performance":[18,149,171,196],"complex":[20,278],"scenarios":[21],"involving":[22],"mixed":[23],"pixels":[24],"spectral":[26],"confusion":[27],"issues.":[28],"This":[29],"paper":[30],"proposes":[31],"a":[32,48,61,265],"Deterministic-Probabilistic":[34],"Dual":[35],"Attention":[36],"Network":[37],"(HDP-Net)":[38],"that":[39,79,268],"addresses":[40],"these":[41],"limitations":[42],"through":[43,103],"three":[44,120],"key":[45],"innovations:":[46],"(1)":[47],"attention":[50,63],"branch":[51,64,90,100],"using":[52],"Sigmoid":[53],"activation":[54],"for":[55,68,203,208,214,242,259],"independent":[56],"feature":[57,76],"weight":[58],"assignment,":[59],"(2)":[60],"probabilistic":[62,99,115],"employing":[65],"temperature-regulated":[66],"Softmax":[67],"controllable":[69],"uncertainty":[70,102,225],"modelling,":[71],"(3)":[73],"an":[74,256],"adaptive":[75],"fusion":[77],"module":[78],"dynamically":[80],"balances":[81],"dual-pathway":[82],"contributions":[83],"based":[84],"on":[85,119,131,145,157],"data":[86],"characteristics.":[87],"The":[88,166,224],"excels":[91],"at":[92],"capturing":[93],"discriminative":[94],"spectral-spatial":[95],"patterns,":[96],"while":[97],"models":[101],"temperature":[105],"parameter":[106],"\u03c4,":[107],"enabling":[108,235],"continuous":[109],"adjustment":[110],"between":[111],"decisions":[113],"exploration.":[116],"Comprehensive":[117],"experiments":[118],"benchmark":[121],"datasets":[122],"demonstrate":[123],"HDP-Net\u2019s":[124],"superior":[125],"performance:":[126],"96.94%":[127],"overall":[128],"accuracy":[129,207,272],"(OA)":[130],"Indian":[132],"Pines":[133],"(0.62%":[134],"improvement":[135,161],"over":[136],"best":[137],"baseline":[138],"with":[139,175,239],"17%":[140],"error":[141,164],"reduction),":[142],"98.39%":[143],"OA":[144,156],"Pavia":[146],"University":[147],"(superior":[148],"challenging":[151,198],"mixed-pixel":[152,209],"scenarios),":[153],"97.08%":[155],"Salinas":[158],"Valley":[159],"(1.28%":[160],"representing":[162],"30.5%":[163],"reduction).":[165],"method":[167],"achieves":[168],"consistent":[169],"top-tier":[170],"across":[172],"diverse":[173],"scenarios,":[174],"average":[176],"accuracies":[177],"94.38%,":[179],"96.92%,":[180],"98.34%,":[182],"Kappa":[184],"coefficients":[185],"96.51%,":[187],"97.87%,":[188],"96.75%,":[190],"respectively.":[191],"Notably,":[192],"HDP-Net":[193,254],"demonstrates":[194],"exceptional":[195],"conditions:":[199],"perfect":[200],"(100%)":[202],"small-sample":[204],"classes,":[205],"83.50%":[206],"\u2018Gravel\u2019":[210],"class":[211],"(vs.":[212],"1.05%\u201366.05%":[213],"competing":[215],"methods),":[216],"robust":[218],"handling":[219],"spectrally":[221],"similar":[222],"categories.":[223],"quantification":[226],"mechanism":[227],"provides":[228],"interpretable":[229],"confidence":[230],"estimates":[231],"alongside":[232],"results,":[234],"automated":[236],"decision-making":[237],"systems":[238],"reliability":[240],"assessment":[241],"precision":[243],"agriculture,":[244],"environmental":[245],"monitoring,":[246],"urban":[248],"planning":[249],"applications.":[250],"These":[251],"results":[252],"establish":[253],"as":[255],"effective":[257],"solution":[258],"uncertainty-aware":[260],"hyperspectral":[261],"classification,":[262],"contributing":[263],"unified":[266],"framework":[267],"advances":[269],"both":[270],"practical":[274],"deployment":[275],"capabilities":[276],"remote":[279],"sensing":[280],"scenarios.":[281]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-01-11T00:00:00"}
