{"id":"https://openalex.org/W2773851128","doi":"https://doi.org/10.1109/igarss.2017.8127429","title":"Hyperspectral image classification via kernel fully constrained least squares","display_name":"Hyperspectral image classification via kernel fully constrained least squares","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2773851128","doi":"https://doi.org/10.1109/igarss.2017.8127429","mag":"2773851128"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2017.8127429","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8127429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5083977325","display_name":"Jianjun Liu","orcid":"https://orcid.org/0000-0001-6155-7898"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianjun Liu","raw_affiliation_strings":["School of Internet of Things Engineering, Jiangnan University, Wuxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Internet of Things Engineering, Jiangnan University, Wuxi, China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031361250","display_name":"Zebin Wu","orcid":"https://orcid.org/0000-0002-7162-0202"},"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":"Zebin Wu","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":null,"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/A5100603146","display_name":"Zhiyong Xiao","orcid":"https://orcid.org/0000-0003-3187-1629"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyong Xiao","raw_affiliation_strings":["School of Internet of Things Engineering, Jiangnan University, Wuxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Internet of Things Engineering, Jiangnan University, Wuxi, China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007738986","display_name":"Jinlong Yang","orcid":"https://orcid.org/0000-0001-9548-4236"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinlong Yang","raw_affiliation_strings":["School of Internet of Things Engineering, Jiangnan University, Wuxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Internet of Things Engineering, Jiangnan University, Wuxi, China","institution_ids":["https://openalex.org/I111599522"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1687,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.77709652,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"49","issue":null,"first_page":"2219","last_page":"2222"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9606000185012817,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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":0.9647589921951294},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7310870885848999},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6732234954833984},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6571161150932312},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6195423007011414},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5451220870018005},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.49735406041145325},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.4536170959472656},{"id":"https://openalex.org/keywords/full-spectral-imaging","display_name":"Full spectral imaging","score":0.4258444607257843},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4239587187767029},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39336931705474854},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11555686593055725}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9647589921951294},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7310870885848999},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6732234954833984},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6571161150932312},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6195423007011414},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5451220870018005},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.49735406041145325},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.4536170959472656},{"id":"https://openalex.org/C78660771","wikidata":"https://www.wikidata.org/wiki/Q5508206","display_name":"Full spectral imaging","level":3,"score":0.4258444607257843},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4239587187767029},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39336931705474854},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11555686593055725},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2017.8127429","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8127429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1997565609","https://openalex.org/W1998030734","https://openalex.org/W2046098657","https://openalex.org/W2077028485","https://openalex.org/W2083541351","https://openalex.org/W2084252873","https://openalex.org/W2091494211","https://openalex.org/W2097915756","https://openalex.org/W2104269704","https://openalex.org/W2127062304","https://openalex.org/W2131864940","https://openalex.org/W2136251662","https://openalex.org/W2164330327","https://openalex.org/W2294492906"],"related_works":["https://openalex.org/W2911259277","https://openalex.org/W4386427838","https://openalex.org/W2533019003","https://openalex.org/W2800956885","https://openalex.org/W2626158795","https://openalex.org/W2057283258","https://openalex.org/W1788560349","https://openalex.org/W2324845311","https://openalex.org/W2391021239","https://openalex.org/W2166564037"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,61],"new":[4],"spatial-spectral":[5,41],"classification":[6,34,51],"method":[7],"for":[8,31],"hyperspectral":[9,27,32,44,96],"images,":[10],"which":[11],"consists":[12],"of":[13,43,53,60,73,101],"three":[14],"main":[15],"techniques.":[16],"Firstly,":[17],"fully":[18],"constrained":[19],"least":[20],"squares":[21],"(FCLS)":[22],"that":[23],"is":[24,29,46,76],"common":[25],"in":[26,35],"unmixing":[28],"investigated":[30],"image":[33],"kernel":[36],"Hilbert":[37],"space.":[38],"Secondly,":[39],"the":[40,50,68,84,99,102],"information":[42,72],"images":[45,97],"exploited":[47],"to":[48,81],"improve":[49],"performance":[52],"kernel-based":[54],"FCLS":[55],"(KFCLS)":[56],"by":[57,87],"taking":[58],"advantage":[59],"weighted":[62],"H1":[63],"norm-based":[64],"regularization":[65],"term.":[66],"Finally,":[67],"spatial":[69],"and":[70],"label":[71],"training":[74,89],"pixels":[75],"furthermore":[77],"incorporated":[78],"into":[79],"KFCLS":[80],"deal":[82],"with":[83],"scenarios":[85],"dominated":[86],"limited":[88],"pixels.":[90],"Experimental":[91],"results":[92],"on":[93],"two":[94],"real":[95],"demonstrate":[98],"effectiveness":[100],"proposed":[103],"method.":[104]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
