{"id":"https://openalex.org/W2983582331","doi":"https://doi.org/10.1109/igarss.2019.8899047","title":"Hyperspectral Compressive Sensing Via Spatial-Spectral Total Variation Regularized Low-Rank Tensor Decomposition","display_name":"Hyperspectral Compressive Sensing Via Spatial-Spectral Total Variation Regularized Low-Rank Tensor Decomposition","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2983582331","doi":"https://doi.org/10.1109/igarss.2019.8899047","mag":"2983582331"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8899047","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8899047","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-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/A5101773688","display_name":"Ting Xie","orcid":"https://orcid.org/0000-0002-0243-3112"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Xie","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067097659","display_name":"Shutao Li","orcid":"https://orcid.org/0000-0002-0585-9848"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shutao Li","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100641761","display_name":"Bin Sun","orcid":"https://orcid.org/0000-0002-7029-8784"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Sun","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I16609230"],"apc_list":null,"apc_paid":null,"fwci":0.1942,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.52947033,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"21","issue":null,"first_page":"1963","last_page":"1966"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"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":0.9977999925613403,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.8975715637207031},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.6219452619552612},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.579957127571106},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5479417443275452},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.5459141731262207},{"id":"https://openalex.org/keywords/tensor-decomposition","display_name":"Tensor decomposition","score":0.5141990184783936},{"id":"https://openalex.org/keywords/variation","display_name":"Variation (astronomy)","score":0.5109845399856567},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4567815065383911},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41871756315231323},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4106835722923279},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3580145835876465},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34061282873153687},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1626606583595276},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.13236531615257263},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.10976240038871765},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.08655494451522827},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.07045280933380127}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8975715637207031},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.6219452619552612},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.579957127571106},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5479417443275452},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.5459141731262207},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.5141990184783936},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.5109845399856567},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4567815065383911},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41871756315231323},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4106835722923279},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3580145835876465},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34061282873153687},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1626606583595276},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.13236531615257263},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.10976240038871765},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.08655494451522827},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.07045280933380127},{"id":"https://openalex.org/C44870925","wikidata":"https://www.wikidata.org/wiki/Q37547","display_name":"Astrophysics","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8899047","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8899047","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1992400371","https://openalex.org/W2075394305","https://openalex.org/W2130120519","https://openalex.org/W2142224912","https://openalex.org/W2170962921","https://openalex.org/W2509021534","https://openalex.org/W2963676935","https://openalex.org/W2964214749"],"related_works":["https://openalex.org/W2781510240","https://openalex.org/W2950186459","https://openalex.org/W2170114491","https://openalex.org/W2569661359","https://openalex.org/W2242624680","https://openalex.org/W2136127937","https://openalex.org/W2897298721","https://openalex.org/W4290987221","https://openalex.org/W2216309014","https://openalex.org/W3199841771"],"abstract_inverted_index":{"Hyperspectral":[0],"compressive":[1],"sensing":[2],"(HCS)":[3],"is":[4,19,55,69,84,111,126],"considered":[5],"for":[6,21,57,60],"reconstructing":[7],"the":[8,22,29,61,63,73,88,106,115,123,129],"hyperspectral":[9],"image":[10,39],"(HSI)":[11],"from":[12],"a":[13,44],"few":[14],"random":[15],"sampled":[16],"measurements.":[17],"HCS":[18],"crucial":[20],"onboard":[23],"imaging":[24],"systems":[25],"to":[26,71,86,113,128],"cut":[27],"down":[28],"acquisition":[30],"time":[31],"and":[32,36,79,93],"data":[33],"storage":[34],"volume,":[35],"simultaneously":[37],"maintain":[38],"quality.":[40],"In":[41,99],"this":[42],"paper,":[43],"spatial-spectral":[45],"total":[46],"variation":[47],"(SSTV)":[48],"regularized":[49],"low-rank":[50],"tensor":[51,64],"decomposition":[52],"(LRTD)":[53],"method":[54,110,125],"proposed":[56,124],"HCS.":[58],"Specifically,":[59],"HSI,":[62],"nuclear":[65],"norm":[66],"based":[67,104],"LRTD":[68],"utilized":[70],"characterize":[72],"global":[74],"correlation":[75,95],"among":[76],"all":[77],"bands,":[78],"an":[80,101],"anisotropic":[81],"SSTV":[82],"regularization":[83],"explored":[85],"describe":[87],"local":[89],"spatial":[90],"smooth":[91],"structure":[92],"spectral":[94],"of":[96],"adjacent":[97],"bands.":[98],"addition,":[100],"efficient":[102],"algorithm":[103],"on":[105],"alternative":[107],"direction":[108],"multiplier":[109],"developed":[112],"solve":[114],"resulting":[116],"optimization":[117],"problem.":[118],"Experimental":[119],"results":[120],"demonstrate":[121],"that":[122],"superior":[127],"existing":[130],"state-of-the-art":[131],"ones.":[132]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
