NAME
i.pca - Principal components analysis (PCA) for image processing.
KEYWORDS
imagery, image transformation, PCA
SYNOPSIS
i.pca
i.pca help
i.pca input=name[,name,...] output_prefix=string [rescale=min,max] [--verbose] [--quiet]
Parameters:
- input=name[,name,...]
- Name of two or more input raster maps
- output_prefix=string
- Base name for output raster maps
- A numerical suffix will be added for each component map
- rescale=min,max
- Rescaling range for output maps
- For no rescaling use 0,0
- Default: 0,255
DESCRIPTION
i.pca is an image processing program based on the
algorithm provided by Vali (1990), that processes n
(n >= 2) input raster map layers and produces n output
raster map layers containing the principal components of
the input data in decreasing order of variance
("contrast"). The output raster map layers are assigned
names with .1, .2, ... .n suffixes. The current geographic
region definition and MASK settings are respected when
reading the input raster map layers. When the rescale
option is used, the output files are rescaled to fit the
min,max range.
OPTIONS
Parameters:
- input=name,name[,name,name,...]
- Name of two or more input raster map layers.
- output=name
- The output raster map layer name to which suffixes are
added. Each output raster map layer is assigned this
user-specified name with a numerical (.1, .2, ...
.n) suffix.
- rescale=min,max
- The optional output category range. (Default: 0,255) If
rescale=0,0, no rescaling is performed on output files.
- If output is rescaled, the output raster will be of type CELL. If
the output is not rescaled, the output raster will be of type DCELL.
NOTES
Richards (1986) gives a good example of the application of principal
components analysis (pca) to a time series of LANDSAT images of a burned
region in Australia.
Eigenvalue and eigenvector information is stored in the output maps'
history files. View with r.info.
EXAMPLE
Using the Spearfish Imagery sample dataset
i.pca in=spot.ms.1,spot.ms.2,spot.ms.3 out=spot_pca
r.info -h spot_pca.1
Eigen values, (vectors), and [percent importance]:
PC1 1170.12 ( -0.63 -0.65 -0.43 ) [ 88.07% ]
PC2 152.49 ( 0.23 0.37 -0.90 ) [ 11.48% ]
PC3 6.01 ( 0.75 -0.66 -0.08 ) [ 0.45% ]
SEE ALSO
Richards, John A.,
Remote Sensing Digital Image Analysis,
Springer-Verlag, 1986.
Vali, Ali R.,
Personal communication,
Space Research Center,
University of Texas, Austin, 1990.
i.cca
i.class
i.fft
i.ifft
m.eigensystem
r.covar
r.mapcalc
AUTHOR
David Satnik, GIS Laboratory
Major modifications for GRASS 4.1 were made by
Olga Waupotitsch and
Michael Shapiro,
U.S.Army Construction Engineering
Research Laboratory
Rewritten for GRASS 6.x and major modifications by
Brad Douglas
Last changed: $Date: 2009-03-02 15:45:37 -0800 (Mon, 02 Mar 2009) $
Main index - imagery index - Full index
© 2003-2011 GRASS Development Team