
 Basic statistics: create table with mean, std dev, min, and
max for each band
 Correlation matrix: show the correlation matrix for each pair
of bands.
 Variancecovariance matrix: show this matrix for each pair of
bands. The correlation matrix is computed from this matrix and
is probably easier to interpret.
 Eigen vectors and PC loadings: shows the relative magnitude
of each eigenvector, and the loadings on each vector of each variable.
 Principal component results: show the PC images.
 Load PC images
 Max PC bands: only this number of new grids will be
created. Get a senses of the number of pick by looking at the
eigen vectors. The 25 shown here only makes sense of hyperspectral
imagery.
 Min explanation to show (%): the higher principal components
typically only show noise, so you can limit the images to display by
specifing the minimium amount of the variation a component must
explain.
