{"id":"https://openalex.org/W2792756998","doi":"https://doi.org/10.1109/jstars.2018.2797052","title":"Unsupervised Kernel Correlations Based Hyperspectral Clustering With Missing Pixels","display_name":"Unsupervised Kernel Correlations Based Hyperspectral Clustering With Missing Pixels","publication_year":2018,"publication_date":"2018-02-19","ids":{"openalex":"https://openalex.org/W2792756998","doi":"https://doi.org/10.1109/jstars.2018.2797052","mag":"2792756998"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2018.2797052","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstars.2018.2797052","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":false,"is_in_doaj":true,"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Kazi Tanzeem Shahid","orcid":"https://orcid.org/0000-0002-6598-5264"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kazi Tanzeem Shahid","raw_affiliation_strings":["Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA"],"raw_orcid":"https://orcid.org/0000-0002-6598-5264","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051935281","display_name":"Akshay Malhotra","orcid":"https://orcid.org/0000-0001-7167-2611"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Akshay Malhotra","raw_affiliation_strings":["Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA"],"raw_orcid":"https://orcid.org/0000-0001-7167-2611","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072740145","display_name":"Ioannis D. Schizas","orcid":"https://orcid.org/0000-0002-1714-5578"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ioannis D. Schizas","raw_affiliation_strings":["Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA"],"raw_orcid":"https://orcid.org/0000-0002-1714-5578","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078640843","display_name":"Saibun Tjuatja","orcid":"https://orcid.org/0000-0003-2614-7927"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saibun Tjuatja","raw_affiliation_strings":["Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA"],"raw_orcid":"https://orcid.org/0000-0003-2614-7927","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA","institution_ids":["https://openalex.org/I189196454"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":null,"fwci":1.8826,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.88004778,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"11","issue":"6","first_page":"1799","last_page":"1810"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9884999990463257,"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":0.9487000107765198,"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":0.8651465773582458},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7590135335922241},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6712441444396973},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6558065414428711},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6376343369483948},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.616595447063446},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5319523811340332},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.46198612451553345},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.42899176478385925},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4174915850162506},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.13050439953804016},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10197323560714722}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8651465773582458},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7590135335922241},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6712441444396973},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6558065414428711},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6376343369483948},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.616595447063446},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5319523811340332},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.46198612451553345},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.42899176478385925},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4174915850162506},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.13050439953804016},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10197323560714722},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jstars.2018.2797052","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstars.2018.2797052","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":false,"is_in_doaj":true,"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1592111270","https://openalex.org/W1965913815","https://openalex.org/W1998030734","https://openalex.org/W2038745668","https://openalex.org/W2042601300","https://openalex.org/W2082600204","https://openalex.org/W2098301339","https://openalex.org/W2099129687","https://openalex.org/W2104269704","https://openalex.org/W2130055251","https://openalex.org/W2131697388","https://openalex.org/W2136454596","https://openalex.org/W2150566919","https://openalex.org/W2150593711","https://openalex.org/W2153409933","https://openalex.org/W2164437025","https://openalex.org/W2248307923","https://openalex.org/W2508058002","https://openalex.org/W2606536620","https://openalex.org/W2617288309","https://openalex.org/W3023786531","https://openalex.org/W4244633107","https://openalex.org/W4301410333","https://openalex.org/W6635264510","https://openalex.org/W6725172435","https://openalex.org/W6736484938"],"related_works":["https://openalex.org/W2089892314","https://openalex.org/W4386075310","https://openalex.org/W2095626363","https://openalex.org/W2169565408","https://openalex.org/W1603091392","https://openalex.org/W2121506664","https://openalex.org/W2127229869","https://openalex.org/W3123056048","https://openalex.org/W2150638158","https://openalex.org/W2363184354"],"abstract_inverted_index":{"This":[0],"paper":[1],"focuses":[2],"on":[3,169],"unsupervised":[4,188],"clustering":[5,108],"of":[6,21,61,147,180,194],"hyperspectral":[7,150,171],"pixels":[8,25,62],"whose":[9,110],"intensity":[10],"may":[11],"not":[12],"be":[13,121,129],"available":[14,97],"across":[15,95],"certain":[16],"spectral":[17,98],"bands.":[18,99],"The":[19,100,137],"presence":[20,193],"statistical":[22],"correlations":[23,55,102,139],"among":[24],"that":[26,88,127,159,174],"contain":[27,160],"data":[28],"originating":[29],"from":[30],"the":[31,75,92,96,115,148,163,175,192],"same":[32,164],"material":[33],"is":[34,50,71,141],"exploited":[35],"here":[36],"to":[37,45,57,73,114],"develop":[38],"a":[39,53,124],"novel":[40,101,138],"regularized":[41,54],"correlation":[42],"analysis":[43],"framework":[44,56,103],"perform":[46],"clustering.":[47],"Kernel":[48],"learning":[49],"integrated":[51],"in":[52,143,178,191],"exploit":[58],"nonlinear":[59],"dependencies":[60],"acquiring":[63],"information":[64,93,161],"about":[65,162],"similar":[66],"materials.":[67,165],"An":[68],"effective":[69],"technique":[70],"proposed":[72,176],"select":[74],"kernel":[76,82],"mapping":[77],"parameters":[78],"and":[79,187],"form":[80],"pertinent":[81],"covariance":[83],"matrices":[84,109,119],"by":[85],"proper":[86,106],"weighted-averaging":[87],"takes":[89],"into":[90],"account":[91],"content":[94],"will":[104,120,128],"return":[105],"sparse":[107],"nonzero":[111],"entries":[112],"point":[113],"correlated":[116],"pixels.":[117,196],"These":[118],"obtained":[122],"via":[123,131],"minimization":[125],"formulation":[126,140],"solved":[130],"computationally":[132],"efficient":[133],"subgradient":[134],"descent":[135],"iterations.":[136],"applied":[142],"small-size":[144],"pixel":[145],"patches":[146,156],"original":[149],"image,":[151],"while":[152],"recursively":[153],"bigger":[154],"size":[155],"are":[157],"built":[158],"Extensive":[166],"numerical":[167],"tests":[168],"real":[170],"images":[172],"reveal":[173],"approach,":[177],"spite":[179],"being":[181],"unsupervised,":[182],"can":[183],"outperform":[184],"existing":[185],"supervised":[186],"techniques":[189],"especially":[190],"missing":[195]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
