{"id":"https://openalex.org/W4402288767","doi":"https://doi.org/10.1109/igarss53475.2024.10640866","title":"Merging Total Variation Into Low-Rank Representation for Hyperspectral Anomaly Detection","display_name":"Merging Total Variation Into Low-Rank Representation for Hyperspectral Anomaly Detection","publication_year":2024,"publication_date":"2024-07-07","ids":{"openalex":"https://openalex.org/W4402288767","doi":"https://doi.org/10.1109/igarss53475.2024.10640866"},"language":"en","primary_location":{"id":"doi:10.1109/igarss53475.2024.10640866","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss53475.2024.10640866","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100721239","display_name":"Linwei Li","orcid":"https://orcid.org/0000-0002-8406-8322"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linwei Li","raw_affiliation_strings":["Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE),Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE),Shanghai,China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083230237","display_name":"Ziyu Wu","orcid":"https://orcid.org/0000-0003-2347-4113"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziyu Wu","raw_affiliation_strings":["Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE),Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE),Shanghai,China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059575276","display_name":"Bin Wang","orcid":"https://orcid.org/0000-0003-4748-6426"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Wang","raw_affiliation_strings":["Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE),Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE),Shanghai,China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083412798","display_name":"Bo Hu","orcid":"https://orcid.org/0000-0001-6348-010X"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Hu","raw_affiliation_strings":["Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE),Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE),Shanghai,China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7991","last_page":"7994"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9955999851226807,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8946144580841064},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7299861907958984},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5817338228225708},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5519751310348511},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5496495962142944},{"id":"https://openalex.org/keywords/variation","display_name":"Variation (astronomy)","score":0.544768750667572},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5222048163414001},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4960261285305023},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4672437012195587},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33846330642700195},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24732446670532227},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07229986786842346},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.05888843536376953}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8946144580841064},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7299861907958984},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5817338228225708},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5519751310348511},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5496495962142944},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.544768750667572},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5222048163414001},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4960261285305023},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4672437012195587},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33846330642700195},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24732446670532227},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07229986786842346},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.05888843536376953},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C44870925","wikidata":"https://www.wikidata.org/wiki/Q37547","display_name":"Astrophysics","level":1,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss53475.2024.10640866","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss53475.2024.10640866","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2047870694","https://openalex.org/W2145962650","https://openalex.org/W2295576075","https://openalex.org/W2972480129","https://openalex.org/W3008839601","https://openalex.org/W3195044646","https://openalex.org/W3199351457","https://openalex.org/W4282929851","https://openalex.org/W4292363360","https://openalex.org/W4294690695"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4377864969","https://openalex.org/W2972971679"],"abstract_inverted_index":{"Utilizing":[0],"the":[1,50,55,77,82,92,98,114],"intrinsic":[2],"low-rank":[3,70],"characteristics":[4],"of":[5,46,81,85,100],"hyperspectral":[6,11,110],"images":[7],"(HSIs)":[8],"to":[9,75],"achieve":[10],"anomaly":[12],"detection":[13,40],"(HAD)":[14],"has":[15,117],"attracted":[16],"great":[17],"interest":[18],"in":[19,36,39,120],"recent":[20],"years.":[21],"However,":[22],"there":[23],"are":[24],"too":[25],"many":[26],"regularizations":[27],"and":[28,79,97,108],"mutual":[29,93],"constraints":[30],"between":[31,95],"regularizers,":[32],"which":[33,88],"would":[34],"result":[35],"a":[37,61],"reduction":[38],"accuracy,":[41],"while":[42],"an":[43],"increasing":[44],"number":[45],"trade-off":[47],"parameters":[48],"complicate":[49],"parameter":[51,101],"tuning.":[52,102],"To":[53],"overcome":[54],"above":[56],"problems,":[57],"this":[58],"paper":[59],"proposes":[60],"novel":[62],"method":[63],"based":[64],"on":[65,105],"merging":[66],"total":[67],"variation":[68],"into":[69],"representation":[71],"(MTVLRR)":[72],"for":[73],"HAD,":[74],"preserve":[76],"low-rankness":[78],"smoothness":[80],"background":[83],"component":[84],"HSIs":[86],"simultaneously,":[87],"can":[89],"significantly":[90],"decrease":[91],"influence":[94],"regularizers":[96],"difficulty":[99],"Experimental":[103],"results":[104],"both":[106],"simulated":[107],"real":[109],"datasets":[111],"demonstrate":[112],"that":[113],"proposed":[115],"MTVLRR":[116],"superior":[118],"performance":[119],"HAD.":[121]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}