{"id":"https://openalex.org/W2983826805","doi":"https://doi.org/10.1109/igarss.2019.8899312","title":"Correlation Alignment Based On Sparse Matrix Transform for Unsupervised Domain Adaptation in Hyperspectral Image Classification","display_name":"Correlation Alignment Based On Sparse Matrix Transform for Unsupervised Domain Adaptation in Hyperspectral Image Classification","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2983826805","doi":"https://doi.org/10.1109/igarss.2019.8899312","mag":"2983826805"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8899312","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8899312","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/A5008099743","display_name":"Tianhui Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I75900474","display_name":"Hubei University","ror":"https://ror.org/03a60m280","country_code":"CN","type":"education","lineage":["https://openalex.org/I75900474"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tianhui Wei","raw_affiliation_strings":["Hubei University,Faculty of Mathematics and Statistics"],"affiliations":[{"raw_affiliation_string":"Hubei University,Faculty of Mathematics and Statistics","institution_ids":["https://openalex.org/I75900474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043696243","display_name":"Wenqi Fan","orcid":"https://orcid.org/0000-0002-4049-1233"},"institutions":[{"id":"https://openalex.org/I75900474","display_name":"Hubei University","ror":"https://ror.org/03a60m280","country_code":"CN","type":"education","lineage":["https://openalex.org/I75900474"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenqi Fan","raw_affiliation_strings":["Hubei University,Faculty of Mathematics and Statistics"],"affiliations":[{"raw_affiliation_string":"Hubei University,Faculty of Mathematics and Statistics","institution_ids":["https://openalex.org/I75900474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036030486","display_name":"Jiangtao Peng","orcid":"https://orcid.org/0000-0002-4759-0584"},"institutions":[{"id":"https://openalex.org/I75900474","display_name":"Hubei University","ror":"https://ror.org/03a60m280","country_code":"CN","type":"education","lineage":["https://openalex.org/I75900474"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangtao Peng","raw_affiliation_strings":["Hubei University,Faculty of Mathematics and Statistics"],"affiliations":[{"raw_affiliation_string":"Hubei University,Faculty of Mathematics and Statistics","institution_ids":["https://openalex.org/I75900474"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024183218","display_name":"Weiwei Sur","orcid":null},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwei Sur","raw_affiliation_strings":["Ningbo University,Department of Geography and Spatial Information Techniques"],"affiliations":[{"raw_affiliation_string":"Ningbo University,Department of Geography and Spatial Information Techniques","institution_ids":["https://openalex.org/I109935558"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5008099743"],"corresponding_institution_ids":["https://openalex.org/I75900474"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18595256,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2698","last_page":"2701"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9977999925613403,"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":0.9977999925613403,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9800000190734863,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9510999917984009,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8437768220901489},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6853780746459961},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6547541618347168},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.6411588788032532},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5825998187065125},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.49970173835754395},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.44438040256500244},{"id":"https://openalex.org/keywords/eigendecomposition-of-a-matrix","display_name":"Eigendecomposition of a matrix","score":0.43810638785362244},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.41864097118377686},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.41546663641929626},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4142301678657532},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.41294533014297485},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.31504926085472107},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3019639551639557},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.281379371881485},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07408970594406128}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8437768220901489},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6853780746459961},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6547541618347168},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.6411588788032532},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5825998187065125},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.49970173835754395},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.44438040256500244},{"id":"https://openalex.org/C169756996","wikidata":"https://www.wikidata.org/wiki/Q194919","display_name":"Eigendecomposition of a matrix","level":3,"score":0.43810638785362244},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.41864097118377686},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.41546663641929626},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4142301678657532},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.41294533014297485},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31504926085472107},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3019639551639557},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.281379371881485},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07408970594406128},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","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},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","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},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8899312","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8899312","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":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.5400000214576721},{"display_name":"Life below water","id":"https://metadata.un.org/sdg/14","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2104068492","https://openalex.org/W2115403315","https://openalex.org/W2121862141","https://openalex.org/W2134594501","https://openalex.org/W2149466042","https://openalex.org/W2155569237","https://openalex.org/W2910979321","https://openalex.org/W2963275094","https://openalex.org/W3154664134","https://openalex.org/W4288076010","https://openalex.org/W6681637710","https://openalex.org/W6683007409","https://openalex.org/W6685415078"],"related_works":["https://openalex.org/W2117760611","https://openalex.org/W2289858865","https://openalex.org/W2005921625","https://openalex.org/W2898722594","https://openalex.org/W2156673141","https://openalex.org/W2150953077","https://openalex.org/W2112519774","https://openalex.org/W2113856999","https://openalex.org/W4391266752","https://openalex.org/W2106353646"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"an":[3,36],"unsupervised":[4,75],"domain":[5,31,76],"adaptation":[6,77],"(DA)":[7],"method":[8,69],"called":[9],"correlation":[10],"alignment":[11],"based":[12,51],"on":[13,52,81],"sparse":[14,44],"matrix":[15,45],"transform":[16],"(CORAL-SMT)":[17],"for":[18],"hyperspectral":[19,86],"image":[20],"(HSI)":[21],"classification.":[22],"In":[23],"CORAL-SMT,":[24],"the":[25,56,82,90],"covariance":[26],"of":[27,84,92],"source":[28],"and":[29,62],"target":[30],"are":[32,63],"constrained":[33],"to":[34],"have":[35],"eigen-decomposition":[37],"that":[38],"can":[39,58],"be":[40,59],"represented":[41],"as":[42],"a":[43],"transform.":[46],"Under":[47],"maximum":[48],"likelihood":[49],"framework,":[50],"greedy":[53],"minimization":[54],"strategy,":[55],"covariances":[57],"efficiently":[60],"estimated":[61],"always":[64],"positive":[65],"definite.":[66],"The":[67],"proposed":[68],"is":[70],"compared":[71],"with":[72],"some":[73],"classical":[74],"methods.":[78],"Experimental":[79],"results":[80],"City":[83],"Pavia":[85],"data":[87],"set":[88],"demonstrate":[89],"effectiveness":[91],"CORAL-SMT.":[93]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
