{"id":"https://openalex.org/W2038930744","doi":"https://doi.org/10.1109/icip.2010.5651568","title":"Discriminative sparse representations in hyperspectral imagery","display_name":"Discriminative sparse representations in hyperspectral imagery","publication_year":2010,"publication_date":"2010-09-01","ids":{"openalex":"https://openalex.org/W2038930744","doi":"https://doi.org/10.1109/icip.2010.5651568","mag":"2038930744"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2010.5651568","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2010.5651568","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Conference on Image Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://conservancy.umn.edu/bitstreams/ede0179d-8792-4a5e-b838-f4bd23e07dd6/download","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061417114","display_name":"Alexey Castrodad","orcid":null},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Alexey Castrodad","raw_affiliation_strings":["University of Minnesota, USA","University Of Minnesota , USA"],"affiliations":[{"raw_affiliation_string":"University of Minnesota, USA","institution_ids":["https://openalex.org/I130238516"]},{"raw_affiliation_string":"University Of Minnesota , USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041900762","display_name":"Zhengming Xing","orcid":null},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhengming Xing","raw_affiliation_strings":["Duke University, USA","[Duke University, USA]"],"affiliations":[{"raw_affiliation_string":"Duke University, USA","institution_ids":["https://openalex.org/I170897317"]},{"raw_affiliation_string":"[Duke University, USA]","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058336082","display_name":"John B. Greer","orcid":null},"institutions":[{"id":"https://openalex.org/I1319949027","display_name":"National Governors Association","ror":"https://ror.org/05pzb3403","country_code":"US","type":"government","lineage":["https://openalex.org/I1319949027"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Greer","raw_affiliation_strings":["NGA, USA"],"affiliations":[{"raw_affiliation_string":"NGA, USA","institution_ids":["https://openalex.org/I1319949027"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056641834","display_name":"Edward H. Bosch","orcid":null},"institutions":[{"id":"https://openalex.org/I1319949027","display_name":"National Governors Association","ror":"https://ror.org/05pzb3403","country_code":"US","type":"government","lineage":["https://openalex.org/I1319949027"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Edward Bosch","raw_affiliation_strings":["NGA, USA"],"affiliations":[{"raw_affiliation_string":"NGA, USA","institution_ids":["https://openalex.org/I1319949027"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016448581","display_name":"Lawrence Carin","orcid":"https://orcid.org/0000-0001-6277-7948"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lawrence Carin","raw_affiliation_strings":["Duke University, USA","[Duke University, USA]"],"affiliations":[{"raw_affiliation_string":"Duke University, USA","institution_ids":["https://openalex.org/I170897317"]},{"raw_affiliation_string":"[Duke University, USA]","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025218580","display_name":"Guillermo Sapiro","orcid":"https://orcid.org/0000-0001-9190-6964"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guillermo Sapiro","raw_affiliation_strings":["University of Minnesota, USA","University Of Minnesota , USA"],"affiliations":[{"raw_affiliation_string":"University of Minnesota, USA","institution_ids":["https://openalex.org/I130238516"]},{"raw_affiliation_string":"University Of Minnesota , USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5061417114"],"corresponding_institution_ids":["https://openalex.org/I130238516"],"apc_list":null,"apc_paid":null,"fwci":5.3607,"has_fulltext":true,"cited_by_count":19,"citation_normalized_percentile":{"value":0.95452872,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1313","last_page":"1316"},"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9890000224113464,"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":0.9207366704940796},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8779187202453613},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7530462145805359},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6898773908615112},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6693894863128662},{"id":"https://openalex.org/keywords/terrain","display_name":"Terrain","score":0.5287023782730103},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5070858001708984},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4722532629966736},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4532403349876404},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.446820467710495},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3737410008907318},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33589714765548706},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.1717168390750885},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09566617012023926},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.059048235416412354}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9207366704940796},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8779187202453613},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7530462145805359},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6898773908615112},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6693894863128662},{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.5287023782730103},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5070858001708984},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4722532629966736},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4532403349876404},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.446820467710495},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3737410008907318},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33589714765548706},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.1717168390750885},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09566617012023926},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.059048235416412354}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icip.2010.5651568","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2010.5651568","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Conference on Image Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:conservancy.umn.edu:11299/180821","is_oa":true,"landing_page_url":"http://hdl.handle.net/11299/180821","pdf_url":"https://conservancy.umn.edu/bitstreams/ede0179d-8792-4a5e-b838-f4bd23e07dd6/download","source":{"id":"https://openalex.org/S4306402521","display_name":"University of Minnesota Digital Conservancy (University of Minnesota)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I130238516","host_organization_name":"University of Minnesota","host_organization_lineage":["https://openalex.org/I130238516"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Preprint"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.184.6151","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.184.6151","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ima.umn.edu/preprints/mar2010/2302.pdf","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:conservancy.umn.edu:11299/180821","is_oa":true,"landing_page_url":"http://hdl.handle.net/11299/180821","pdf_url":"https://conservancy.umn.edu/bitstreams/ede0179d-8792-4a5e-b838-f4bd23e07dd6/download","source":{"id":"https://openalex.org/S4306402521","display_name":"University of Minnesota Digital Conservancy (University of Minnesota)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I130238516","host_organization_name":"University of Minnesota","host_organization_lineage":["https://openalex.org/I130238516"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.7900000214576721,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2038930744.pdf","grobid_xml":"https://content.openalex.org/works/W2038930744.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W1567856076","https://openalex.org/W1976709621","https://openalex.org/W1998433034","https://openalex.org/W2003217181","https://openalex.org/W2082855665","https://openalex.org/W2122922389","https://openalex.org/W2128638419","https://openalex.org/W2129812935","https://openalex.org/W2135046866","https://openalex.org/W2140433132","https://openalex.org/W2160547390","https://openalex.org/W2169210068","https://openalex.org/W2295820431","https://openalex.org/W2313986711","https://openalex.org/W2953139536","https://openalex.org/W4233760599","https://openalex.org/W6633757495","https://openalex.org/W6678757208","https://openalex.org/W6681029311"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2070598848","https://openalex.org/W3034375524","https://openalex.org/W2027399350","https://openalex.org/W2060875994","https://openalex.org/W2044184146","https://openalex.org/W2019190440","https://openalex.org/W2761785940","https://openalex.org/W2971377935"],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2],"sparse":[3,74],"modeling":[4],"and":[5,70,109],"dictionary":[6],"learning":[7],"for":[8,14,66,73,85,95,107],"discriminative":[9],"applications":[10],"show":[11,22],"high":[12],"potential":[13],"numerous":[15],"classification":[16,27,58],"tasks.":[17],"In":[18],"this":[19],"paper,":[20],"we":[21],"that":[23,64],"highly":[24],"accurate":[25],"material":[26],"from":[28,44],"hyperspectral":[29,101],"imagery":[30],"(HSI)":[31],"can":[32],"be":[33],"obtained":[34],"with":[35,99],"these":[36],"models,":[37],"even":[38],"when":[39],"the":[40,50,82,86],"data":[41,102],"is":[42,59,93],"reconstructed":[43],"a":[45,62,89],"very":[46],"small":[47],"percentage":[48],"of":[49],"original":[51],"image":[52],"samples.":[53],"The":[54],"proposed":[55],"supervised":[56],"HSI":[57],"performed":[60],"using":[61],"measure":[63],"accounts":[65],"both":[67,106],"reconstruction":[68],"errors":[69],"sparsity":[71],"levels":[72],"representations":[75],"based":[76],"on":[77],"class-dependent":[78],"learned":[79,84],"dictionaries.":[80],"Combining":[81],"dictionaries":[83],"different":[87],"materials,":[88],"linear":[90],"mixing":[91],"model":[92],"derived":[94],"sub-pixel":[96],"classification.":[97],"Results":[98],"real":[100],"cubes":[103],"are":[104],"shown":[105],"urban":[108],"non-urban":[110],"terrain.":[111]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":3}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}