{"doi":"10.33899/iqjoss.2013.75422","title":"Using the Canonical Correlation Analysis Technique for Imaging Dimensionality Reduction in Multisource  Land sat Images","abstract":null,"journal":"IRAQI JOURNAL OF STATISTICAL SCIENCES","year":2013,"id":620749,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Using the Canonical Correlation Analysis Technique for Imaging Dimensionality Reduction in Multisource  Land sat Images","abstract":"The Canonical Correlations Analysis technique (CCA) was suggested in the dimensionality reduction images for the multivariate multisource data applied in remote sensing . These techniques transform multivariate multiset data into new orthogonal variables called Canonical Variates (CVs) . This research uses the LANDSAT-5 TM data for the set of multivariate multispectral correlation at fixed points in time . The results show maximum similarity for the low- order canonical variates and minimum similarity for the high- order canonical variates .","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W3089323894","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.56063918,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://stats.mosuljournals.com/article_75422_994084f6beed8792826349c2b72da5ed.pdf","host_type":"journal"},{"url":"https://stats.mosuljournals.com/article_75422_994084f6beed8792826349c2b72da5ed.pdf","host_type":"publisher"},{"url":"https://doi.org/10.33899/iqjoss.2013.75422","host_type":"journal"},{"url":"https://doaj.org/article/9592eb88d2ab4e3dad9c2bc8e3634b95","host_type":"repository"}],"fields_of_study":["Remote-Sensing Image Classification","Geochemistry and Geologic Mapping"],"mesh_terms":[],"keywords":["Canonical correlation","Multiset","Dimensionality reduction","Multivariate statistics","Canonical analysis","Curse of dimensionality","Multispectral image","Principal component analysis","Data set","Similarity (geometry)","Mathematics","Multivariate analysis","Pattern recognition (psychology)","Canonical correspondence analysis","Artificial intelligence","Set (abstract data type)","Correlation","Computer science","Statistics","Combinatorics","Image (mathematics)"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-03T12:02:24.188619Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}