{"id":"https://openalex.org/W2625436554","doi":"https://doi.org/10.1109/tgrs.2017.2704590","title":"Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification","display_name":"Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification","publication_year":2017,"publication_date":"2017-06-14","ids":{"openalex":"https://openalex.org/W2625436554","doi":"https://doi.org/10.1109/tgrs.2017.2704590","mag":"2625436554"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2017.2704590","doi":"https://doi.org/10.1109/tgrs.2017.2704590","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2017.2704590","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,"listed_in":["anvur-scientific","ccf-b","cwts-core","dongbi-b","jufo-3","norway-2","poland-200","russia-white-list-1","vabb-shw"],"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"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1912.10803","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007556914","display_name":"Vanika Singhal","orcid":"https://orcid.org/0000-0002-9773-1488","observed_orcids":["https://orcid.org/0000-0002-9773-1488"]},"institutions":[{"id":"https://openalex.org/I119939252","display_name":"Indraprastha Institute of Information Technology Delhi","ror":"https://ror.org/03vfp4g33","country_code":"IN","type":"education","lineage":["https://openalex.org/I119939252"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vanika Singhal","raw_affiliation_strings":["Indraprastha Institute of Information Technology at Delhi, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indraprastha Institute of Information Technology at Delhi, New Delhi, India","institution_ids":["https://openalex.org/I119939252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076929884","display_name":"Hemant Kumar Aggarwal","orcid":"https://orcid.org/0000-0002-3756-9765","observed_orcids":["https://orcid.org/0000-0002-3756-9765"]},"institutions":[{"id":"https://openalex.org/I119939252","display_name":"Indraprastha Institute of Information Technology Delhi","ror":"https://ror.org/03vfp4g33","country_code":"IN","type":"education","lineage":["https://openalex.org/I119939252"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Hemant K. Aggarwal","raw_affiliation_strings":["Indraprastha Institute of Information Technology at Delhi, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indraprastha Institute of Information Technology at Delhi, New Delhi, India","institution_ids":["https://openalex.org/I119939252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060728957","display_name":"Snigdha Tariyal","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I119939252","display_name":"Indraprastha Institute of Information Technology Delhi","ror":"https://ror.org/03vfp4g33","country_code":"IN","type":"education","lineage":["https://openalex.org/I119939252"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Snigdha Tariyal","raw_affiliation_strings":["Indraprastha Institute of Information Technology at Delhi, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indraprastha Institute of Information Technology at Delhi, New Delhi, India","institution_ids":["https://openalex.org/I119939252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020310463","display_name":"Angshul Majumdar","orcid":"https://orcid.org/0000-0002-1065-3000","observed_orcids":["https://orcid.org/0000-0002-1065-3000"]},"institutions":[{"id":"https://openalex.org/I119939252","display_name":"Indraprastha Institute of Information Technology Delhi","ror":"https://ror.org/03vfp4g33","country_code":"IN","type":"education","lineage":["https://openalex.org/I119939252"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Angshul Majumdar","raw_affiliation_strings":["Indraprastha Institute of Information Technology at Delhi, New Delhi, India"],"raw_orcid":"https://orcid.org/0000-0002-1065-3000","affiliations":[{"raw_affiliation_string":"Indraprastha Institute of Information Technology at Delhi, New Delhi, India","institution_ids":["https://openalex.org/I119939252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I119939252"],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":3.2945,"has_fulltext":true,"cited_by_count":53,"citation_normalized_percentile":{"value":0.95131029,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"55","issue":"9","first_page":"5274","last_page":"5283"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9984999895095825,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9984999895095825,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.00019999999494757503,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":9.999999747378752e-05,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-dictionary-learning","display_name":"deep dictionary learning","score":0.8659999966621399},{"id":"https://openalex.org/keywords/hyperspectral-image-classification","display_name":"hyperspectral image classification","score":0.9919999837875366},{"id":"https://openalex.org/keywords/discriminative-dictionary","display_name":"discriminative dictionary","score":0.6290000081062317},{"id":"https://openalex.org/keywords/mixed-noise","display_name":"mixed noise","score":0.8040000200271606},{"id":"https://openalex.org/keywords/sparse-noise","display_name":"sparse noise","score":0.8730000257492065},{"id":"https://openalex.org/keywords/sparse-representation-based-classification","display_name":"sparse representation classification","score":0.3019999861717224}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8182414770126343},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7516530752182007},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7152969837188721},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7096121907234192},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.707850992679596},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7063372135162354},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7057139277458191},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7002986669540405},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6336504817008972},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6290271878242493},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4932994246482849},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.46684229373931885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3347599506378174},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1781441867351532},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/tgrs.2017.2704590","doi":"https://doi.org/10.1109/tgrs.2017.2704590","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2017.2704590","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,"listed_in":["anvur-scientific","ccf-b","cwts-core","dongbi-b","jufo-3","norway-2","poland-200","russia-white-list-1","vabb-shw"],"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"},{"id":"pmh:oai:arXiv.org:1912.10803","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1912.10803","pdf_url":"https://arxiv.org/pdf/1912.10803","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2996522231","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/pdf/1912.10803.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1912.10803","doi":"https://doi.org/10.48550/arxiv.1912.10803","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1912.10803","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1912.10803","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1912.10803","pdf_url":"https://arxiv.org/pdf/1912.10803","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2625436554.pdf","grobid_xml":"https://content.openalex.org/works/W2625436554.grobid-xml"},"referenced_works_count":57,"referenced_works":["https://openalex.org/W189596042","https://openalex.org/W647651355","https://openalex.org/W1522345735","https://openalex.org/W1799366690","https://openalex.org/W1902027874","https://openalex.org/W1970169482","https://openalex.org/W1971707173","https://openalex.org/W1976359033","https://openalex.org/W1999829977","https://openalex.org/W2027805700","https://openalex.org/W2028623420","https://openalex.org/W2029316659","https://openalex.org/W2042092375","https://openalex.org/W2046033161","https://openalex.org/W2046793177","https://openalex.org/W2054912984","https://openalex.org/W2056385906","https://openalex.org/W2061056108","https://openalex.org/W2068861059","https://openalex.org/W2072128103","https://openalex.org/W2078264890","https://openalex.org/W2086847993","https://openalex.org/W2087134730","https://openalex.org/W2090424610","https://openalex.org/W2099866409","https://openalex.org/W2106005123","https://openalex.org/W2106334384","https://openalex.org/W2109999391","https://openalex.org/W2115429828","https://openalex.org/W2115706991","https://openalex.org/W2128741343","https://openalex.org/W2129812935","https://openalex.org/W2131725398","https://openalex.org/W2135848147","https://openalex.org/W2136437631","https://openalex.org/W2145035344","https://openalex.org/W2146842127","https://openalex.org/W2151122377","https://openalex.org/W2160547390","https://openalex.org/W2163398148","https://openalex.org/W2164122462","https://openalex.org/W2179290474","https://openalex.org/W2254140767","https://openalex.org/W2291068538","https://openalex.org/W2314785379","https://openalex.org/W2337872419","https://openalex.org/W2342750892","https://openalex.org/W2344173806","https://openalex.org/W2345128667","https://openalex.org/W2405123320","https://openalex.org/W2518897583","https://openalex.org/W2538244214","https://openalex.org/W2548791488","https://openalex.org/W2766947113","https://openalex.org/W2963793388","https://openalex.org/W4231109964","https://openalex.org/W4256161595"],"related_works":["https://openalex.org/W2901819993","https://openalex.org/W2766947113","https://openalex.org/W4229367296","https://openalex.org/W2552220127","https://openalex.org/W782681081","https://openalex.org/W4313042514","https://openalex.org/W270302467","https://openalex.org/W4402915461","https://openalex.org/W2743193290","https://openalex.org/W2515621898"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,25,35,43,45],"new":[4],"framework":[5],"for":[6,14,69],"deep":[7,109,129],"learning":[8,110,130],"that":[9,33,102],"has":[10],"been":[11,123],"particularly":[12],"tailored":[13],"hyperspectral":[15,98,145],"image":[16],"classification.":[17,70],"We":[18],"learn":[19],"multiple":[20],"levels":[21],"of":[22,48,80],"dictionaries":[23],"in":[24,97],"robust":[26],"fashion.":[27],"The":[28,38,61,86,120],"last":[29],"layer":[30],"is":[31,50,72],"discriminative":[32],"learns":[34],"linear":[36],"classifier.":[37],"training":[39],"proceeds":[40],"greedily;":[41],"at":[42],"time,":[44],"single":[46],"level":[47,66],"dictionary":[49],"learned":[51],"and":[52,94,116,135],"the":[53,58,64,76,81],"coefficients":[54,62],"used":[55,68],"to":[56],"train":[57],"next":[59],"level.":[60],"from":[63],"final":[65],"are":[67],"Robustness":[71],"incorporated":[73],"by":[74],"minimizing":[75],"absolute":[77],"deviations":[78],"instead":[79],"more":[82],"popular":[83],"Euclidean":[84],"norm.":[85],"inbuilt":[87],"robustness":[88],"helps":[89],"combat":[90],"mixed":[91],"noise":[92],"(Gaussian":[93],"sparse)":[95],"present":[96],"images.":[99],"Results":[100],"show":[101],"our":[103],"proposed":[104],"techniques":[105],"outperform":[106],"all":[107],"other":[108],"methods-deep":[111],"belief":[112],"network,":[113],"stacked":[114],"autoencoder,":[115],"convolutional":[117],"neural":[118],"network.":[119],"experiments":[121],"have":[122],"carried":[124],"out":[125],"on":[126,143],"both":[127],"benchmark":[128],"data":[131,147],"sets":[132],"(MNIST,":[133],"CIFAR-10,":[134],"Street":[136],"View":[137],"House":[138],"Numbers)":[139],"as":[140,142],"well":[141],"real":[144],"imaging":[146],"sets.":[148]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":13},{"year":2018,"cited_by_count":12},{"year":2017,"cited_by_count":2}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2017-06-23T00:00:00"}