{"id":"https://openalex.org/W2765191464","doi":"https://doi.org/10.1109/whispers.2016.8071739","title":"Hyperspectral image classification with sparse representation classifier and active learning","display_name":"Hyperspectral image classification with sparse representation classifier and active learning","publication_year":2016,"publication_date":"2016-08-01","ids":{"openalex":"https://openalex.org/W2765191464","doi":"https://doi.org/10.1109/whispers.2016.8071739","mag":"2765191464"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2016.8071739","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2016.8071739","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","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/A5040034589","display_name":"Lianzhi Huo","orcid":"https://orcid.org/0000-0001-6705-6453"},"institutions":[{"id":"https://openalex.org/I4210128053","display_name":"Institute of Remote Sensing and Digital Earth","ror":"https://ror.org/02cjszf03","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128053"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lian-Zhi Huo","raw_affiliation_strings":["Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210128053","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066891690","display_name":"Lijun Zhao","orcid":"https://orcid.org/0000-0002-7140-8105"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210128053","display_name":"Institute of Remote Sensing and Digital Earth","ror":"https://ror.org/02cjszf03","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128053"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li-Jun Zhao","raw_affiliation_strings":["Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210128053","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101748090","display_name":"Ping Tang","orcid":"https://orcid.org/0000-0002-8721-4209"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210128053","display_name":"Institute of Remote Sensing and Digital Earth","ror":"https://ror.org/02cjszf03","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128053"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Tang","raw_affiliation_strings":["Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210128053","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5040034589"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210128053"],"apc_list":null,"apc_paid":null,"fwci":0.323,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.7104553,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"6","issue":null,"first_page":"1","last_page":"5"},"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9926000237464905,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9854000210762024,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8070423007011414},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7533897757530212},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7162862420082092},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.7041875720024109},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6733307242393494},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6510821580886841},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5485315322875977},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5349053144454956},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.45368942618370056},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.44120460748672485},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.34510165452957153}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8070423007011414},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7533897757530212},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7162862420082092},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.7041875720024109},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6733307242393494},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6510821580886841},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5485315322875977},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5349053144454956},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.45368942618370056},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.44120460748672485},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.34510165452957153},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers.2016.8071739","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2016.8071739","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5400000214576721,"display_name":"Quality Education"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1664950380","https://openalex.org/W1968591910","https://openalex.org/W1997025799","https://openalex.org/W2025263547","https://openalex.org/W2038386419","https://openalex.org/W2038851698","https://openalex.org/W2045900692","https://openalex.org/W2082397227","https://openalex.org/W2097915756","https://openalex.org/W2100835628","https://openalex.org/W2101711129","https://openalex.org/W2107131609","https://openalex.org/W2129587305","https://openalex.org/W2129812935","https://openalex.org/W2134663338","https://openalex.org/W2139823104","https://openalex.org/W2150045166","https://openalex.org/W2153409933","https://openalex.org/W2161494756","https://openalex.org/W2213075807","https://openalex.org/W2762869430","https://openalex.org/W3099014258","https://openalex.org/W4250800088","https://openalex.org/W6637241018","https://openalex.org/W6680434193"],"related_works":["https://openalex.org/W2374021060","https://openalex.org/W2044488462","https://openalex.org/W2981877337","https://openalex.org/W3203938600","https://openalex.org/W2169074127","https://openalex.org/W2163707935","https://openalex.org/W83146503","https://openalex.org/W2005234362","https://openalex.org/W202723009","https://openalex.org/W1997235926"],"abstract_inverted_index":{"Sparse":[0],"representation":[1,15,95],"classifiers":[2,16],"have":[3,81],"been":[4,82],"widely":[5],"studied":[6,88],"for":[7,93,114],"hyperspectral":[8,125],"image":[9],"classification.":[10],"The":[11,97,118,127],"success":[12],"of":[13,26,33,60,67,100,132],"sparse":[14,94],"depends":[17],"highly":[18],"on":[19,57,122],"the":[20,24,31,43,51,58,61,65,101,107,130,133],"training":[21,27,54],"dictionary.":[22],"However,":[23],"definition":[25],"samples,":[28],"often":[29],"in":[30],"form":[32],"field":[34],"investigations,":[35],"is":[36,104],"time":[37],"consuming":[38],"and":[39],"costly.":[40],"To":[41],"mitigate":[42],"problem,":[44],"active":[45,78,90],"learning":[46,79,91],"tries":[47],"to":[48,69,105],"iteratively":[49],"define":[50],"most":[52,110],"informative":[53],"samples":[55,68,108],"based":[56],"outputs":[59],"classifiers,":[62],"thus":[63],"reducing":[64],"quantities":[66],"be":[70],"labeled.":[71],"For":[72],"different":[73,77,116],"classification":[74],"models,":[75],"several":[76],"strategies":[80],"proposed.":[83],"In":[84],"this":[85],"paper,":[86],"we":[87],"one":[89],"strategy":[92],"classifiers.":[96],"main":[98],"idea":[99],"proposed":[102,134],"algorithm":[103],"select":[106],"with":[109],"similar":[111],"reconstruction":[112],"errors":[113],"two":[115,123],"classes.":[117],"experiments":[119],"are":[120],"performed":[121],"public":[124],"data.":[126],"results":[128],"show":[129],"effectiveness":[131],"algorithm.":[135]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}