{"id":"https://openalex.org/W4404971995","doi":"https://doi.org/10.3390/rs16234518","title":"The Spectrum Difference Enhanced Network for Hyperspectral Anomaly Detection","display_name":"The Spectrum Difference Enhanced Network for Hyperspectral Anomaly Detection","publication_year":2024,"publication_date":"2024-12-02","ids":{"openalex":"https://openalex.org/W4404971995","doi":"https://doi.org/10.3390/rs16234518"},"language":"en","primary_location":{"id":"doi:10.3390/rs16234518","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16234518","pdf_url":"https://www.mdpi.com/2072-4292/16/23/4518/pdf?version=1733205804","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/2072-4292/16/23/4518/pdf?version=1733205804","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021156437","display_name":"Shaohua Liu","orcid":"https://orcid.org/0009-0001-1856-6999"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shaohua Liu","raw_affiliation_strings":["Faculty of Engineering, School of Computer Science, The University of Sydney, Camperdown, NSW 2006, Australia"],"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, School of Computer Science, The University of Sydney, Camperdown, NSW 2006, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101241836","display_name":"Huibo Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huibo Guo","raw_affiliation_strings":["School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China","School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China","institution_ids":["https://openalex.org/I4210136859"]},{"raw_affiliation_string":"School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062253896","display_name":"Shiwen Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiwen Gao","raw_affiliation_strings":["School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China","School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China","institution_ids":["https://openalex.org/I4210136859"]},{"raw_affiliation_string":"School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035190973","display_name":"Wuxia Zhang","orcid":"https://orcid.org/0000-0002-0759-2489"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wuxia Zhang","raw_affiliation_strings":["School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China","School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China","institution_ids":["https://openalex.org/I4210136859"]},{"raw_affiliation_string":"School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China","institution_ids":["https://openalex.org/I4210136859"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5035190973"],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":0.31,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.65154764,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"16","issue":"23","first_page":"4518","last_page":"4518"},"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":0.9855999946594238,"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":0.9319999814033508,"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":0.8308005332946777},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7660002708435059},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6575272679328918},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6154544353485107},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5989915728569031},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5928247570991516},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5736802816390991},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5301937460899353},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.46640199422836304},{"id":"https://openalex.org/keywords/named-graph","display_name":"Named graph","score":0.44390103220939636},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.38240694999694824},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3170955777168274}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8308005332946777},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7660002708435059},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6575272679328918},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6154544353485107},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5989915728569031},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5928247570991516},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5736802816390991},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5301937460899353},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.46640199422836304},{"id":"https://openalex.org/C110893760","wikidata":"https://www.wikidata.org/wiki/Q3115590","display_name":"Named graph","level":5,"score":0.44390103220939636},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.38240694999694824},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3170955777168274},{"id":"https://openalex.org/C147497476","wikidata":"https://www.wikidata.org/wiki/Q54872","display_name":"RDF","level":3,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C41009113","wikidata":"https://www.wikidata.org/wiki/Q54871","display_name":"SPARQL","level":4,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/rs16234518","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16234518","pdf_url":"https://www.mdpi.com/2072-4292/16/23/4518/pdf?version=1733205804","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:115bd263424644688038b073377e8f82","is_oa":true,"landing_page_url":"https://doaj.org/article/115bd263424644688038b073377e8f82","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 16, Iss 23, p 4518 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/rs16234518","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16234518","pdf_url":"https://www.mdpi.com/2072-4292/16/23/4518/pdf?version=1733205804","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/G675307798","display_name":null,"funder_award_id":"62471389","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":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4404971995.pdf","grobid_xml":"https://content.openalex.org/works/W4404971995.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W1996118086","https://openalex.org/W2004491663","https://openalex.org/W2034803951","https://openalex.org/W2047870694","https://openalex.org/W2163129097","https://openalex.org/W2288752886","https://openalex.org/W2295576075","https://openalex.org/W2952956606","https://openalex.org/W3022140654","https://openalex.org/W3034493263","https://openalex.org/W3123098349","https://openalex.org/W3137199127","https://openalex.org/W3138516171","https://openalex.org/W3186256209","https://openalex.org/W3199351457","https://openalex.org/W3205538325","https://openalex.org/W4212863773","https://openalex.org/W4212884756","https://openalex.org/W4213019189","https://openalex.org/W4214493665","https://openalex.org/W4221059680","https://openalex.org/W4247093658","https://openalex.org/W4292672241","https://openalex.org/W4307502605","https://openalex.org/W4313007769","https://openalex.org/W4327663519","https://openalex.org/W4384342766","https://openalex.org/W4390187403","https://openalex.org/W4391759484","https://openalex.org/W6739901393","https://openalex.org/W6791818776","https://openalex.org/W6841970600","https://openalex.org/W6850617462","https://openalex.org/W6859988025"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W2072166414","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W3186512740","https://openalex.org/W3194885736","https://openalex.org/W4363671829"],"abstract_inverted_index":{"Most":[0],"deep":[1,132],"learning-based":[2],"hyperspectral":[3,14,23],"anomaly":[4],"detection":[5,214],"(HAD)":[6],"methods":[7,27],"focus":[8],"on":[9,102,203],"modeling":[10,147],"or":[11],"reconstructing":[12],"the":[13,21,35,39,64,67,72,103,119,158,189,198],"background":[15,47,54],"to":[16,34,93,126,144,156,187],"obtain":[17],"residual":[18],"maps":[19],"from":[20],"original":[22],"images.":[24],"However,":[25],"these":[26],"typically":[28],"do":[29],"not":[30],"pay":[31],"enough":[32],"attention":[33],"spectral":[36,159],"similarity":[37],"in":[38,43,154],"complex":[40],"environment,":[41],"resulting":[42],"inadequate":[44],"distinction":[45],"between":[46,162],"and":[48,53,91,115,122,133,164,192,206,216],"anomalies.":[49],"Moreover,":[50],"some":[51],"anomalies":[52,163],"are":[55,60,124,182],"different":[56],"objects,":[57],"but":[58],"they":[59],"sometimes":[61],"recognized":[62],"as":[63],"objects":[65],"with":[66,149,184,221],"same":[68],"spectrum.":[69],"To":[70],"address":[71],"issues":[73],"mentioned":[74],"above,":[75],"this":[76],"paper":[77],"proposes":[78],"a":[79,108,137,166],"Spectrum":[80],"Difference":[81],"Enhanced":[82],"Network":[83],"(SDENet)":[84],"for":[85,172],"HAD,":[86],"which":[87,106],"employs":[88],"variational":[89],"mapping":[90,180],"Transformer":[92],"amplify":[94],"spectrum":[95],"differences.":[96],"The":[97,195],"proposed":[98,199],"network":[99],"is":[100,142,170,201],"based":[101],"encoder\u2013decoder":[104],"structure,":[105],"contains":[107],"CSWin-Transformer":[109,116,120],"encoder,":[110],"Variational":[111],"Mapping":[112],"Module":[113],"(VMModule),":[114],"decoder.":[117],"First,":[118],"encoder":[121],"decoder":[123],"designed":[125,143],"supplement":[127],"image":[128],"information":[129],"by":[130],"extracting":[131],"semantic":[134],"features,":[135],"where":[136],"cross-shaped":[138],"window":[139],"self-attention":[140],"mechanism":[141],"provide":[145],"strong":[146],"capability":[148],"minimal":[150],"computational":[151,193],"cost.":[152],"Second,":[153],"order":[155],"enhance":[157],"difference":[160],"characteristics":[161],"background,":[165],"randomly":[167],"sampling":[168],"VMModule":[169],"presented":[171],"feature":[173],"space":[174],"transformation.":[175],"Finally,":[176],"all":[177],"fully":[178],"connected":[179],"operations":[181],"replaced":[183],"convolutional":[185],"layers":[186],"reduce":[188],"model":[190,218],"parameters":[191],"load.":[194],"effectiveness":[196],"of":[197],"SDENet":[200],"verified":[202],"three":[204],"datasets,":[205],"experimental":[207],"results":[208],"show":[209],"that":[210],"it":[211],"achieves":[212],"better":[213],"accuracy":[215],"lower":[217],"complexity":[219],"compared":[220],"existing":[222],"methods.":[223]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
