{"id":"https://openalex.org/W2985908994","doi":"https://doi.org/10.1109/igarss.2019.8898080","title":"Hyperspectral Image Super-Resolution Classification with a Small Training Set Using Spectral Variation Extended Endmember Library","display_name":"Hyperspectral Image Super-Resolution Classification with a Small Training Set Using Spectral Variation Extended Endmember Library","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2985908994","doi":"https://doi.org/10.1109/igarss.2019.8898080","mag":"2985908994"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8898080","doi":"https://doi.org/10.1109/igarss.2019.8898080","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8898080","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5100376972","display_name":"Yifan Zhang","orcid":"https://orcid.org/0000-0003-4533-3880","observed_orcids":["https://orcid.org/0000-0003-4533-3880","https://orcid.org/0000-0002-2125-1074"]},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifan Zhang","raw_affiliation_strings":["Shaanxi Key Laboratory of Information Acquisition and Processing, Northwestern Polytechnical University, Xi\u2019an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shaanxi Key Laboratory of Information Acquisition and Processing, Northwestern Polytechnical University, Xi\u2019an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046921192","display_name":"Tianqing Zhao","orcid":"https://orcid.org/0009-0007-7372-0040","observed_orcids":["https://orcid.org/0009-0007-7372-0040"]},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianqing Zhao","raw_affiliation_strings":["Shaanxi Key Laboratory of Information Acquisition and Processing, Northwestern Polytechnical University, Xi\u2019an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shaanxi Key Laboratory of Information Acquisition and Processing, Northwestern Polytechnical University, Xi\u2019an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068434646","display_name":"Bobo Xie","orcid":"https://orcid.org/0000-0003-0195-8790","observed_orcids":["https://orcid.org/0000-0003-0195-8790"]},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bobo Xie","raw_affiliation_strings":["Shaanxi Key Laboratory of Information Acquisition and Processing, Northwestern Polytechnical University, Xi\u2019an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shaanxi Key Laboratory of Information Acquisition and Processing, Northwestern Polytechnical University, Xi\u2019an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067207818","display_name":"Shaohui Mei","orcid":"https://orcid.org/0000-0002-8018-596X","observed_orcids":["https://orcid.org/0000-0002-8018-596X"]},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaohui Mei","raw_affiliation_strings":["Shaanxi Key Laboratory of Information Acquisition and Processing, Northwestern Polytechnical University, Xi\u2019an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shaanxi Key Laboratory of Information Acquisition and Processing, Northwestern Polytechnical University, Xi\u2019an, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":"4","issue":null,"first_page":"3001","last_page":"3004"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9990000128746033,"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":0.9990000128746033,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.00039999998989515007,"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":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/hyperspectral-image-classification","display_name":"hyperspectral image classification","score":0.9309999942779541},{"id":"https://openalex.org/keywords/limited-training-data","display_name":"limited training data","score":0.7829999923706055},{"id":"https://openalex.org/keywords/spectral-variability","display_name":"spectral variability","score":0.824999988079071},{"id":"https://openalex.org/keywords/mixed-pixels","display_name":"mixed pixels","score":0.8700000047683716},{"id":"https://openalex.org/keywords/collaborative-representation","display_name":"collaborative representation","score":0.9959999918937683},{"id":"https://openalex.org/keywords/sub-pixel-mapping","display_name":"sub-pixel mapping","score":0.8659999966621399}],"concepts":[{"id":"https://openalex.org/C58237817","wikidata":"https://www.wikidata.org/wiki/Q5376204","display_name":"Endmember","level":3,"score":0.9247493743896484},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9062734842300415},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5745789408683777},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5586658716201782},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5568161606788635},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5470089912414551},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5244024991989136},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5040096640586853},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.48159167170524597},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4642335772514343},{"id":"https://openalex.org/C141239990","wikidata":"https://www.wikidata.org/wiki/Q957423","display_name":"Superresolution","level":3,"score":0.45525112748146057},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4355233311653137},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.42405280470848083},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.26368215680122375},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06167098879814148},{"id":"https://openalex.org/C44870925","wikidata":"https://www.wikidata.org/wiki/Q37547","display_name":"Astrophysics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8898080","doi":"https://doi.org/10.1109/igarss.2019.8898080","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8898080","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2026999441","https://openalex.org/W2050809401","https://openalex.org/W2087874437","https://openalex.org/W2766048519","https://openalex.org/W2766915010","https://openalex.org/W4253856589"],"related_works":["https://openalex.org/W2955023562","https://openalex.org/W3210120301","https://openalex.org/W2171374001","https://openalex.org/W2548685720","https://openalex.org/W2945768950","https://openalex.org/W2613575128","https://openalex.org/W1985468858","https://openalex.org/W3000495970","https://openalex.org/W2736600340","https://openalex.org/W2919561513"],"abstract_inverted_index":{"Classification":[0],"has":[1],"been":[2],"one":[3],"of":[4,9,18,44,59,110,184,189],"the":[5,14,19,36,98,132,149,178],"most":[6],"important":[7],"applications":[8],"hyperspectral":[10],"images":[11],"(HSIs)":[12],"in":[13,33,53,70,92,103],"past":[15],"decade,":[16],"because":[17,58],"outstanding":[20],"discrimination":[21],"among":[22],"different":[23],"classes":[24],"ensured":[25],"by":[26,42],"abundant":[27],"and":[28,113,172],"detailed":[29],"spectral":[30,119,125],"information":[31],"enclosed":[32],"HSIs.":[34],"While":[35],"classification":[37,76,88,150,187],"accuracy":[38],"must":[39],"be":[40,51],"guaranteed":[41],"plenty":[43],"training":[45,111],"samples,":[46],"which":[47,72,131],"is":[48,90,121,156,182],"difficult":[49],"to":[50,95,123],"satisfied":[52],"many":[54],"practical":[55],"cases.":[56],"Meanwhile,":[57],"its":[60],"comparatively":[61],"low":[62,190],"spatial":[63,154,160],"resolution,":[64],"mixed":[65,116,140],"pixels":[66],"are":[67,142,168],"widely":[68,114],"existed":[69,115],"HSIs":[71],"makes":[73],"subpixel":[74,159,164],"level":[75],"techniques":[77],"more":[78],"preferable":[79],"rather":[80],"than":[81],"traditional":[82],"pixel-level":[83],"ones.":[84],"A":[85],"novel":[86],"super-resolution":[87,186],"method":[89,181],"proposed":[91,180],"this":[93],"paper":[94],"deal":[96],"with":[97,130,152,158,193],"two":[99],"above":[100],"mentioned":[101],"problems":[102],"HSI":[104,192],"classification,":[105],"that":[106,177],"is,":[107],"limited":[108],"number":[109],"samples":[112],"pixels.":[117],"Specifically,":[118],"variation":[120,126],"considered":[122],"construct":[124],"extended":[127],"endmember":[128],"library,":[129],"abundance":[133],"fractions":[134],"for":[135,170],"each":[136],"class":[137],"within":[138],"a":[139],"pixel":[141],"estimated":[143],"using":[144],"collaborative":[145],"representation.":[146],"And":[147],"finally,":[148],"result":[151],"higher":[153],"resolution":[155,191],"obtained":[157],"attraction":[161],"model":[162],"based":[163],"mapping.":[165],"Simulative":[166],"experiments":[167],"employed":[169],"validation":[171],"comparison.":[173],"Experimental":[174],"results":[175],"illustrate":[176],"newly":[179],"capable":[183],"producing":[185],"map":[188],"less":[194],"misclassification.":[195]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-10-30T12:39:25.321706","created_date":"2019-11-22T00:00:00"}
