gen_principal_comp_trans — Compute the transformation matrix of the principal component
analysis of multichannel images.
gen_principal_comp_trans computes the transformation matrix
of a principal components analysis of multichannel images. This is
useful for images obtained, e.g., with the thematic mapper of the
Landsat satellite. Because the spectral bands are highly correlated,
it is desirable to transform them to uncorrelated images. This can
be used to save storage, since the bands containing little
information can be discarded, and with respect to a later
gen_principal_comp_trans takes one or more
MultichannelImage and computes the
Trans for the principal components
analysis, as well as its inverse
TransInv. All input
images must have the same number of channels. The principal
components analysis is performed based on the collection of data of
all images. Hence,
using the statistics of multiple images.
If n is the number of channels,
TransInv are matrices of dimension n ×
(n+1), which describe an affine transformation of the multichannel
gray values. They can be used to transform a multichannel image
linear_trans_color. For information purposes, the mean
gray value of the channels and the n × n
covariance matrix of the channels are returned in
Cov, respectively. The parameter
contains the relative information content of each output channel.
Note that filter operators may return unexpected results if an image with a reduced domain is used as input. Please refer to the chapter Filters.
→object (byte / direction / cyclic / int1 / int2 / uint2 / int4 / real)
Multichannel input image.
Transformation matrix for the computation of the PCA.
Transformation matrix for the computation of the inverse PCA.
Mean gray value of the channels.
Covariance matrix of the channels.
Information content of the transformed channels.
gen_principal_comp_trans returns the value 2 (H_MSG_TRUE)
if the parameters are correct. Otherwise an exception is raised.