{"id":"https://openalex.org/W3186256209","doi":"https://doi.org/10.1109/tgrs.2021.3097097","title":"Hyperspectral Anomaly Detection With Robust Graph Autoencoders","display_name":"Hyperspectral Anomaly Detection With Robust Graph Autoencoders","publication_year":2021,"publication_date":"2021-07-26","ids":{"openalex":"https://openalex.org/W3186256209","doi":"https://doi.org/10.1109/tgrs.2021.3097097","mag":"3186256209"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2021.3097097","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2021.3097097","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005262571","display_name":"Ganghui Fan","orcid":"https://orcid.org/0000-0002-6246-2273"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ganghui Fan","raw_affiliation_strings":["Electronic Information School, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-6246-2273","affiliations":[{"raw_affiliation_string":"Electronic Information School, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002618865","display_name":"Yong Ma","orcid":"https://orcid.org/0000-0002-1116-0662"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Ma","raw_affiliation_strings":["Electronic Information School, Wuhan University, Wuhan, China","Institute of Aerospace Science and Technology, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-1116-0662","affiliations":[{"raw_affiliation_string":"Electronic Information School, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]},{"raw_affiliation_string":"Institute of Aerospace Science and Technology, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021381864","display_name":"Xiaoguang Mei","orcid":"https://orcid.org/0000-0002-0239-8580"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoguang Mei","raw_affiliation_strings":["Electronic Information School, Wuhan University, Wuhan, China","Institute of Aerospace Science and Technology, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-0239-8580","affiliations":[{"raw_affiliation_string":"Electronic Information School, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]},{"raw_affiliation_string":"Institute of Aerospace Science and Technology, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100366841","display_name":"Fan Fan","orcid":"https://orcid.org/0000-0002-7507-1810"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Fan","raw_affiliation_strings":["Electronic Information School, Wuhan University, Wuhan, China","Institute of Aerospace Science and Technology, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-7507-1810","affiliations":[{"raw_affiliation_string":"Electronic Information School, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]},{"raw_affiliation_string":"Institute of Aerospace Science and Technology, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002872664","display_name":"Jun Huang","orcid":"https://orcid.org/0000-0001-5893-4090"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Huang","raw_affiliation_strings":["Electronic Information School, Wuhan University, Wuhan, China","Institute of Aerospace Science and Technology, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-5893-4090","affiliations":[{"raw_affiliation_string":"Electronic Information School, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]},{"raw_affiliation_string":"Institute of Aerospace Science and Technology, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040010053","display_name":"Jiayi Ma","orcid":"https://orcid.org/0000-0003-3264-3265"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiayi Ma","raw_affiliation_strings":["Electronic Information School, Wuhan University, Wuhan, China","Institute of Aerospace Science and Technology, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-3264-3265","affiliations":[{"raw_affiliation_string":"Electronic Information School, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]},{"raw_affiliation_string":"Institute of Aerospace Science and Technology, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":null,"apc_paid":null,"fwci":11.6356,"has_fulltext":false,"cited_by_count":197,"citation_normalized_percentile":{"value":0.98929611,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"14"},"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9664000272750854,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9623000025749207,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.8400169014930725},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7656561136245728},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6446571350097656},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6203777194023132},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5940147638320923},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5900834202766418},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4702177047729492},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46486955881118774},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.1498071253299713},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.09226158261299133}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8400169014930725},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7656561136245728},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6446571350097656},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6203777194023132},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5940147638320923},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5900834202766418},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4702177047729492},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46486955881118774},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.1498071253299713},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.09226158261299133}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2021.3097097","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2021.3097097","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G253954187","display_name":null,"funder_award_id":"62061160370","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6362778646","display_name":null,"funder_award_id":"61903279","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7876097311","display_name":null,"funder_award_id":"62003247","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8929883844","display_name":null,"funder_award_id":"62075169","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":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W2004491663","https://openalex.org/W2017014096","https://openalex.org/W2024288510","https://openalex.org/W2037034832","https://openalex.org/W2047870694","https://openalex.org/W2053090063","https://openalex.org/W2056935845","https://openalex.org/W2087263574","https://openalex.org/W2118246710","https://openalex.org/W2124267685","https://openalex.org/W2124463804","https://openalex.org/W2288752886","https://openalex.org/W2295576075","https://openalex.org/W2343117455","https://openalex.org/W2346506533","https://openalex.org/W2497075055","https://openalex.org/W2511630356","https://openalex.org/W2547840382","https://openalex.org/W2566928557","https://openalex.org/W2583890742","https://openalex.org/W2615977073","https://openalex.org/W2734426270","https://openalex.org/W2743138268","https://openalex.org/W2765366036","https://openalex.org/W2766992066","https://openalex.org/W2773583860","https://openalex.org/W2791514264","https://openalex.org/W2796629918","https://openalex.org/W2807662216","https://openalex.org/W2900199428","https://openalex.org/W2903916334","https://openalex.org/W2912147220","https://openalex.org/W2914527789","https://openalex.org/W2969635036","https://openalex.org/W2972480129","https://openalex.org/W2972614519","https://openalex.org/W2975506318","https://openalex.org/W2983563481","https://openalex.org/W2985448050","https://openalex.org/W2987228832","https://openalex.org/W3007098358","https://openalex.org/W3011852491","https://openalex.org/W3016244469","https://openalex.org/W3022940334","https://openalex.org/W3035441328","https://openalex.org/W3121629662","https://openalex.org/W3134743586","https://openalex.org/W3160068210","https://openalex.org/W3168931281"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W2072166414","https://openalex.org/W3186512740","https://openalex.org/W2076134148","https://openalex.org/W3194885736","https://openalex.org/W4363671829","https://openalex.org/W2891286602","https://openalex.org/W2084942241"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,50,215],"of":[2,105,182],"hyperspectral":[3,52,111,222],"data":[4],"has":[5,212],"been":[6],"gaining":[7],"particular":[8],"attention":[9],"for":[10,48,195],"its":[11,24],"ability":[12,87],"in":[13,16,51,66,102,110,135],"detecting":[14],"targets":[15],"an":[17],"unsupervised":[18],"manner.":[19],"Autoencoder":[20],"(AE),":[21],"together":[22],"with":[23,219],"variants":[25],"can":[26,71,77,172],"not":[27],"only":[28],"extract":[29],"intrinsic":[30],"features":[31],"automatically":[32],"but":[33,55],"also":[34,78,186],"detect":[35],"anomalies":[36,65,90,155],"that":[37,75,149,209],"differ":[38],"dramatically":[39],"from":[40,58,91],"others.":[41],"Many":[42],"AE-driven":[43],"algorithms":[44],"are,":[45],"thus,":[46],"proposed":[47],"anomaly":[49,112,122,223],"imagery":[53],"(HSI),":[54],"they":[56],"suffer":[57],"two":[59],"problems:":[60],"1)":[61],"when":[62],"there":[63],"exist":[64],"the":[67,80,86,92,126,174,178,189,206],"training":[68],"set,":[69],"AE":[70,127,132,145],"generalize":[72],"so":[73],"well":[74],"it":[76],"learn":[79],"abnormal":[81],"patterns":[82],"well,":[83],"thereby":[84],"reducing":[85],"to":[88,152],"distinguish":[89],"background":[93],"and":[94,154,177,185,192,205],"2)":[95],"geometric":[96,175],"structure":[97,176],"among":[98],"samples":[99],"are":[100,200],"lost":[101],"latent":[103],"space":[104,191],"AE,":[106],"which":[107],"is":[108,150],"vital":[109],"detection.":[113],"To":[114,138],"tackle":[115],"these":[116],"problems,":[117],"we":[118,141,159],"propose":[119,142],"a":[120,143,161,213],"robust":[121,130,144,151],"detector":[123],"based":[124],"on":[125,202],"framework,":[128],"named":[129],"graph":[131,164],"(RGAE)":[133],"detector,":[134],"this":[136],"article.":[137],"be":[139],"specific,":[140],"framework":[146],"with$\\ell":[147],"_{2,1}$-norm":[148],"noise":[153],"during":[156],"training.":[157],"Meanwhile,":[158],"embed":[160],"superpixel":[162],"segmentation-based":[163],"regularization":[165],"term":[166],"(SuperGraph)":[167],"into":[168],"AE.":[169],"This":[170],"strategy":[171],"preserve":[173],"local":[179],"spatial":[180],"consistency":[181],"HSI":[183],"simultaneously":[184],"effectively":[187],"reduce":[188],"searching":[190],"execution":[193],"time":[194],"each":[196],"pixel.":[197],"Extensive":[198],"experiments":[199],"conducted":[201],"five":[203],"datasets,":[204],"results":[207],"demonstrate":[208],"our":[210],"method":[211],"better":[214],"performance,":[216],"after":[217],"comparing":[218],"other":[220],"state-of-the-art":[221],"detectors.":[224]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":67},{"year":2024,"cited_by_count":59},{"year":2023,"cited_by_count":41},{"year":2022,"cited_by_count":18}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
