PCA has been used in remote sensing for different purposes. A comprehensive summary of
different applications of PCA, including correlation analysis of TM images for effective feature
recognition and change detection with multi-temporal images, is presented in [28]. PCA is a
coordinate transformation typically used to remove the correlation contained within the multiband
imagery by creating a new set of components, which are often more interpretable than the
original images. PCA images thus generated are uncorrelated and ordered by decreasing variance.
The covariance matrix of the transformed data is a diagonal matrix of which the elements are
composed of the eigenvalues.