{"id":"https://openalex.org/W2985905250","doi":"https://doi.org/10.1109/igarss.2019.8899896","title":"Hyperspectral Image Classification Via Tensor Ridge Regression","display_name":"Hyperspectral Image Classification Via Tensor Ridge Regression","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2985905250","doi":"https://doi.org/10.1109/igarss.2019.8899896","mag":"2985905250"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8899896","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8899896","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":"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/A5018617993","display_name":"Jianjun Liu","orcid":"https://orcid.org/0000-0003-0778-9094"},"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/A5100353576","display_name":"Hao Chen","orcid":"https://orcid.org/0000-0002-6418-3761"},"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":"Hao Chen","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/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"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Criminal Science and Technology, Nanjing Forest Police College, Nanjing, China","institution_ids":["https://openalex.org/I4210090357"]}]},{"author_position":"middle","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"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100644375","display_name":"Hong Yan","orcid":"https://orcid.org/0000-0001-9661-3095"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Hong Yan","raw_affiliation_strings":["Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20763889,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1156","last_page":"1159"},"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":0.9968000054359436,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9799000024795532,"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":0.8920955061912537},{"id":"https://openalex.org/keywords/ridge","display_name":"Ridge","score":0.7074518799781799},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6759316921234131},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6530255675315857},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6478796005249023},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.601945698261261},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5862494707107544},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5381252765655518},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5377234816551208},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.42964810132980347},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4150294363498688},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4051913022994995},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3591375946998596},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3362559378147125},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.32175755500793457},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.3043111562728882},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.20948311686515808},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14081332087516785}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8920955061912537},{"id":"https://openalex.org/C32277403","wikidata":"https://www.wikidata.org/wiki/Q740445","display_name":"Ridge","level":2,"score":0.7074518799781799},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6759316921234131},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6530255675315857},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6478796005249023},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.601945698261261},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5862494707107544},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5381252765655518},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5377234816551208},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.42964810132980347},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4150294363498688},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4051913022994995},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3591375946998596},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3362559378147125},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32175755500793457},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.3043111562728882},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.20948311686515808},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14081332087516785},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8899896","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8899896","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1998030734","https://openalex.org/W2001298023","https://openalex.org/W2024165284","https://openalex.org/W2104269704","https://openalex.org/W2109064712","https://openalex.org/W2127199143","https://openalex.org/W2131864940","https://openalex.org/W2164330327","https://openalex.org/W2294492906","https://openalex.org/W2344373810","https://openalex.org/W2500751094","https://openalex.org/W2743255627","https://openalex.org/W2757177965","https://openalex.org/W2759880437","https://openalex.org/W3106141888"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W3148196241","https://openalex.org/W1606169643","https://openalex.org/W1977912248","https://openalex.org/W3092773549","https://openalex.org/W2122926592"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"investigate":[4],"the":[5,39,46,53,60,74,90,93],"ridge":[6,28,41],"regression":[7,29,42],"for":[8,32,79],"multivariate":[9],"labels":[10],"by":[11],"modelling":[12],"each":[13],"pixel":[14],"and":[15,23],"its":[16],"surrounding":[17],"pixels":[18],"as":[19],"a":[20,26],"3D":[21],"tensor,":[22],"thereby":[24],"propose":[25],"tensor":[27],"approach":[30],"(TRR)":[31],"spatial-spectral":[33,55],"hyperspectral":[34,87],"image":[35],"classification.":[36],"Compared":[37],"with":[38,73],"traditional":[40],"model,":[43],"not":[44],"only":[45],"spatial":[47],"information":[48],"is":[49,57,64],"incorporated,":[50],"but":[51],"also":[52],"intrinsic":[54],"structure":[56],"captured.":[58],"Moreover,":[59],"proposed":[61,94],"TRR":[62],"method":[63],"universal":[65],"that":[66],"it":[67],"can":[68],"be":[69],"adopted":[70],"to":[71],"deal":[72],"fusion":[75],"of":[76,92],"multiscale":[77],"features":[78],"classification":[80],"purpose.":[81],"Experiment":[82],"results":[83],"conducted":[84],"on":[85],"two":[86],"scenes":[88],"demonstrate":[89],"effectiveness":[91],"method.":[95]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}