Using the Canonical Correlation Analysis Technique for Imaging Dimensionality Reduction in Multisource Land sat Images

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 .