{"id":"https://openalex.org/W2984452968","doi":"https://doi.org/10.1109/igarss.2019.8900523","title":"Clustering Hyperspectral Images Via Sparse Dictionary Learning with Joint Sparsity and Shared Wavelets","display_name":"Clustering Hyperspectral Images Via Sparse Dictionary Learning with Joint Sparsity and Shared Wavelets","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2984452968","doi":"https://doi.org/10.1109/igarss.2019.8900523","mag":"2984452968"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8900523","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8900523","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/A5014878552","display_name":"Nan Huang","orcid":"https://orcid.org/0000-0003-0871-158X"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Nan Huang","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020302879","display_name":"Liang Xiao","orcid":"https://orcid.org/0000-0003-0178-9384"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Xiao","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062542061","display_name":"Songze Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210090357","display_name":"Nanjing Forest Police College","ror":"https://ror.org/00adax290","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210090357"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Songze Tang","raw_affiliation_strings":["Department of Criminal Science and Technology, Nanjing Forest Police College, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Department of Criminal Science and Technology, Nanjing Forest Police College, Nanjing, China","institution_ids":["https://openalex.org/I4210090357"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063646463","display_name":"Qichao Liu","orcid":"https://orcid.org/0000-0003-0134-9450"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qichao Liu","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5014878552"],"corresponding_institution_ids":["https://openalex.org/I36399199"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18791482,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"803","last_page":"806"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"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":0.9998000264167786,"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":0.992900013923645,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9782000184059143,"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":0.8828166723251343},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7596530914306641},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7009727954864502},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6936273574829102},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6912592649459839},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.6718276739120483},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.6508364081382751},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5824620723724365},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.5179209113121033},{"id":"https://openalex.org/keywords/k-svd","display_name":"K-SVD","score":0.5102696418762207},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4314810335636139},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.06763306260108948}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8828166723251343},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7596530914306641},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7009727954864502},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6936273574829102},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6912592649459839},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.6718276739120483},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.6508364081382751},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5824620723724365},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.5179209113121033},{"id":"https://openalex.org/C154771677","wikidata":"https://www.wikidata.org/wiki/Q17098361","display_name":"K-SVD","level":3,"score":0.5102696418762207},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4314810335636139},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.06763306260108948},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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.2019.8900523","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8900523","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":[{"display_name":"Quality Education","score":0.6800000071525574,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1993962865","https://openalex.org/W2038386419","https://openalex.org/W2056621966","https://openalex.org/W2099321050","https://openalex.org/W2130835014","https://openalex.org/W2161160262","https://openalex.org/W2162409952","https://openalex.org/W2273553327","https://openalex.org/W2313932751","https://openalex.org/W2586344098"],"related_works":["https://openalex.org/W2099321050","https://openalex.org/W2890952311","https://openalex.org/W2374021060","https://openalex.org/W2509955295","https://openalex.org/W2047275718","https://openalex.org/W2034957211","https://openalex.org/W2388952560","https://openalex.org/W110819671","https://openalex.org/W2149282631","https://openalex.org/W2011611369"],"abstract_inverted_index":{"Sparse":[0],"subspace":[1],"clustering":[2,127,138],"(SSC)":[3],"algorithm":[4,147],"has":[5],"achieved":[6],"an":[7],"impressive":[8],"performances":[9],"in":[10,53],"hyperspectral":[11,36,50,102,154],"images":[12,51],"clustering.":[13],"However,":[14],"the":[15,24,33,64,68,74,105,111,131,136,145,153],"raw":[16],"samples":[17],"contained":[18],"noises":[19],"were":[20],"used":[21],"to":[22,62,89,117,130,134],"construct":[23,118],"dictionary.":[25,70],"Moreover,":[26],"SSC":[27],"represented":[28],"each":[29],"signal":[30],"individually":[31],"ignoring":[32],"relationship":[34],"among":[35],"pixels.":[37],"To":[38],"overcome":[39],"these":[40],"problems,":[41],"we":[42,72],"propose":[43],"a":[44,78,82,91,119],"sparse":[45,93,106,113],"dictionary":[46,80,94,114],"learning":[47],"method":[48],"for":[49],"clustering,":[52],"which":[54],"joint":[55,84],"sparsity":[56,85],"and":[57,98],"shared":[58,75],"Wavelets":[59,76],"are":[60,115],"integrated":[61],"improve":[63],"expressive":[65],"power":[66],"of":[67,101,123],"learnt":[69,112],"First,":[71],"incorporate":[73],"as":[77],"base":[79],"into":[81],"unified":[83],"constrained":[86],"optimizing":[87],"model":[88],"learn":[90],"structured":[92],"from":[95],"both":[96],"spectral":[97,126],"contextual":[99],"characteristics":[100],"images.":[103],"Then,":[104],"representation":[107],"coefficients":[108],"based":[109],"on":[110,152],"adopted":[116],"non-negative":[120],"affinity":[121,132],"matrix":[122,133],"graph.":[124],"Finally,":[125],"is":[128],"employed":[129],"obtain":[135],"final":[137],"result.":[139],"Experimental":[140],"results":[141],"clearly":[142],"demonstrate":[143],"that":[144],"proposed":[146],"outperforms":[148],"other":[149],"state-of-the-art":[150],"methods":[151],"dataset.":[155]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
