{"id":"https://openalex.org/W4313576755","doi":"https://doi.org/10.3390/rs15020304","title":"Low-Rank Constrained Attention-Enhanced Multiple Spatial\u2013Spectral Feature Fusion for Small Sample Hyperspectral Image Classification","display_name":"Low-Rank Constrained Attention-Enhanced Multiple Spatial\u2013Spectral Feature Fusion for Small Sample Hyperspectral Image Classification","publication_year":2023,"publication_date":"2023-01-04","ids":{"openalex":"https://openalex.org/W4313576755","doi":"https://doi.org/10.3390/rs15020304"},"language":"en","primary_location":{"id":"doi:10.3390/rs15020304","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15020304","pdf_url":"https://www.mdpi.com/2072-4292/15/2/304/pdf?version=1673855065","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/15/2/304/pdf?version=1673855065","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072479287","display_name":"Fan Feng","orcid":"https://orcid.org/0000-0003-1344-3727"},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fan Feng","raw_affiliation_strings":["PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"],"affiliations":[{"raw_affiliation_string":"PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China","institution_ids":["https://openalex.org/I169689159"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100374669","display_name":"Yongsheng Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongsheng Zhang","raw_affiliation_strings":["PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"],"affiliations":[{"raw_affiliation_string":"PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China","institution_ids":["https://openalex.org/I169689159"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100406011","display_name":"Jin Zhang","orcid":"https://orcid.org/0000-0003-3036-3075"},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Zhang","raw_affiliation_strings":["PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"],"affiliations":[{"raw_affiliation_string":"PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China","institution_ids":["https://openalex.org/I169689159"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039991862","display_name":"Bing Liu","orcid":"https://orcid.org/0000-0003-0848-8453"},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bing Liu","raw_affiliation_strings":["PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"],"affiliations":[{"raw_affiliation_string":"PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China","institution_ids":["https://openalex.org/I169689159"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5072479287"],"corresponding_institution_ids":["https://openalex.org/I169689159"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":2.1999,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.88735112,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"15","issue":"2","first_page":"304","last_page":"304"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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":1.0,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9811999797821045,"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":0.8370881080627441},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6855227947235107},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6740986704826355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6664730310440063},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4948996901512146},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4847847819328308},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4503534734249115},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.44765010476112366},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43304887413978577},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4290882349014282},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.35718271136283875},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.24484091997146606},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.08637771010398865}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8370881080627441},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6855227947235107},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6740986704826355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6664730310440063},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4948996901512146},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4847847819328308},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4503534734249115},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.44765010476112366},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43304887413978577},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4290882349014282},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.35718271136283875},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.24484091997146606},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.08637771010398865},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs15020304","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15020304","pdf_url":"https://www.mdpi.com/2072-4292/15/2/304/pdf?version=1673855065","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:54d7134dff5e49b5b249601bc492c46d","is_oa":true,"landing_page_url":"https://doaj.org/article/54d7134dff5e49b5b249601bc492c46d","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 15, Iss 2, p 304 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/15/2/304/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs15020304","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 15; Issue 2; Pages: 304","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs15020304","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15020304","pdf_url":"https://www.mdpi.com/2072-4292/15/2/304/pdf?version=1673855065","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1452172099","display_name":null,"funder_award_id":"42071340","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G736076830","display_name":null,"funder_award_id":"221100211000-01","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"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4313576755.pdf"},"referenced_works_count":62,"referenced_works":["https://openalex.org/W1966580635","https://openalex.org/W2500751094","https://openalex.org/W2554320282","https://openalex.org/W2614256707","https://openalex.org/W2757208835","https://openalex.org/W2764276316","https://openalex.org/W2792332881","https://openalex.org/W2801324747","https://openalex.org/W2822065499","https://openalex.org/W2884585870","https://openalex.org/W2894165434","https://openalex.org/W2898204262","https://openalex.org/W2914331134","https://openalex.org/W2962702700","https://openalex.org/W2963420686","https://openalex.org/W2963495494","https://openalex.org/W2977002487","https://openalex.org/W2980598835","https://openalex.org/W2982083293","https://openalex.org/W2991575127","https://openalex.org/W2997294353","https://openalex.org/W3003552243","https://openalex.org/W3012405452","https://openalex.org/W3014253313","https://openalex.org/W3017845037","https://openalex.org/W3021805542","https://openalex.org/W3031696400","https://openalex.org/W3046027728","https://openalex.org/W3047317383","https://openalex.org/W3049655825","https://openalex.org/W3086507804","https://openalex.org/W3099850646","https://openalex.org/W3100932715","https://openalex.org/W3103753223","https://openalex.org/W3114720220","https://openalex.org/W3114798932","https://openalex.org/W3120356488","https://openalex.org/W3122028341","https://openalex.org/W3122774149","https://openalex.org/W3138725786","https://openalex.org/W3177052299","https://openalex.org/W3192416785","https://openalex.org/W3192731655","https://openalex.org/W3198155834","https://openalex.org/W3207818847","https://openalex.org/W3212386989","https://openalex.org/W3213631637","https://openalex.org/W3214821343","https://openalex.org/W3216860461","https://openalex.org/W4207006012","https://openalex.org/W4210524687","https://openalex.org/W4220727424","https://openalex.org/W4226334005","https://openalex.org/W4229042884","https://openalex.org/W4281568429","https://openalex.org/W4285127355","https://openalex.org/W4292257555","https://openalex.org/W4312443924","https://openalex.org/W4312743284","https://openalex.org/W6797691975","https://openalex.org/W6803226058","https://openalex.org/W6804650247"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W2991587282","https://openalex.org/W4288018740","https://openalex.org/W3008919350","https://openalex.org/W2095030957","https://openalex.org/W2066827917"],"abstract_inverted_index":{"Hyperspectral":[0],"images":[1],"contain":[2],"rich":[3],"features":[4,61],"in":[5,35],"both":[6],"spectral":[7,113],"and":[8,20,53,76,129,167,192,209,228,242,270,285],"spatial":[9],"domains,":[10],"which":[11,234],"bring":[12],"opportunities":[13],"for":[14,125,218],"accurate":[15],"recognition":[16],"of":[17,38,224,238,252,261,297],"similar":[18],"materials":[19],"promote":[21],"various":[22],"fine-grained":[23],"remote":[24],"sensing":[25],"applications.":[26],"Although":[27],"deep":[28,126],"learning":[29,291],"models":[30,201,273],"have":[31],"been":[32,56],"extensively":[33],"investigated":[34],"the":[36,105,130,163,231,246,258,264,271,295,298],"field":[37],"hyperspectral":[39,177],"image":[40],"classification":[41,44],"(HSIC)":[42],"tasks,":[43],"performance":[45],"is":[46,95,123,133,159,254],"still":[47],"limited":[48],"under":[49],"small":[50],"sample":[51],"conditions,":[52],"this":[54],"has":[55,235],"a":[57,83,117,136,150],"longstanding":[58],"problem.":[59],"The":[60,140,195,249],"extracted":[62],"by":[63],"complex":[64],"network":[65,90],"structures":[66],"with":[67,179,287],"large":[68],"model":[69,300],"size":[70],"are":[71,147],"redundant":[72],"to":[73,78,97,111,161,245,278],"some":[74,288],"extent":[75],"prone":[77],"overfitting.":[79],"This":[80],"paper":[81],"proposes":[82],"low-rank":[84,156],"constrained":[85],"attention-enhanced":[86,119],"multiple":[87],"feature":[88,114,127,145,165],"fusion":[89],"(LAMFN).":[91],"Firstly,":[92],"factor":[93],"analysis":[94],"used":[96,124],"extract":[98],"very":[99],"few":[100],"components":[101],"that":[102],"can":[103],"describe":[104],"original":[106],"data":[107],"using":[108,135],"covariance":[109],"information":[110,132],"perform":[112],"preprocessing.":[115],"Then,":[116],"lightweight":[118],"3D":[120],"convolution":[121],"module":[122],"extraction,":[128],"position-sensitive":[131],"supplemented":[134],"2D":[137],"coordinate":[138],"attention.":[139],"above":[141,232],"widely":[142],"varying":[143],"spatial\u2013spectral":[144,181],"groups":[146],"fused":[148],"through":[149],"simple":[151],"composite":[152],"residual":[153,205],"structure.":[154],"Finally,":[155],"second-order":[157],"pooling":[158],"adopted":[160],"enhance":[162],"convolutional":[164],"selectivity":[166],"achieve":[168],"classification.":[169],"Extensive":[170],"experiments":[171,284],"were":[172],"conducted":[173],"on":[174,230,263],"four":[175],"representative":[176],"datasets":[178],"different":[180],"characteristics,":[182],"namely":[183],"Indian":[184],"Pines":[185],"(IP),":[186],"Pavia":[187],"Center":[188],"(PC),":[189],"Houston":[190],"(HU),":[191],"WHU-HongHu":[193],"(WHU).":[194],"contrast":[196,272],"methods":[197,292],"include":[198],"several":[199],"advanced":[200,289],"proposed":[202,299],"recently,":[203],"including":[204],"CNNs,":[206,208],"attention-based":[207],"transformer-based":[210],"models.":[211],"Using":[212],"only":[213],"five":[214],"samples":[215],"per":[216],"class":[217],"training,":[219],"LAMFN":[220,253,262],"achieved":[221],"overall":[222],"accuracies":[223],"78.15%,":[225],"97.18%,":[226],"81.35%,":[227],"87.93%":[229],"datasets,":[233],"an":[236],"improvement":[237],"0.82%,":[239],"1.12%,":[240],"1.67%,":[241],"0.89%":[243],"compared":[244],"second-best":[247],"model.":[248],"running":[250],"time":[251,260],"moderate.":[255],"For":[256],"example,":[257],"training":[259],"WHU":[265],"dataset":[266],"was":[267],"29.1":[268],"s,":[269],"ranged":[274],"from":[275],"3.0":[276],"s":[277],"341.4":[279],"s.":[280],"In":[281],"addition,":[282],"ablation":[283],"comparisons":[286],"semi-supervised":[290],"further":[293],"validated":[294],"effectiveness":[296],"designs.":[301]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3}],"updated_date":"2026-04-15T08:11:43.952461","created_date":"2023-01-06T00:00:00"}