.TH i.pca 1grass "" "GRASS 6.4.2" "Grass User's Manual"
.SH NAME
\fI\fBi.pca\fR\fR  - Principal components analysis (PCA) for image processing.
.SH KEYWORDS
imagery, image transformation, PCA
.SH SYNOPSIS
\fBi.pca\fR
.br
\fBi.pca help\fR
.br
\fBi.pca input\fR=\fIname\fR[,\fIname\fR,...] \fBoutput_prefix\fR=\fIstring\fR  [\fBrescale\fR=\fImin,max\fR]   [--\fBverbose\fR]  [--\fBquiet\fR] 
.SS Parameters:
.IP "\fBinput\fR=\fIname[,\fIname\fR,...]\fR" 4m
.br
Name of two or more input raster maps
.IP "\fBoutput_prefix\fR=\fIstring\fR" 4m
.br
Base name for output raster maps
.br
A numerical suffix will be added for each component map
.IP "\fBrescale\fR=\fImin,max\fR" 4m
.br
Rescaling range for output maps
.br
For no rescaling use 0,0
.br
Default: \fI0,255\fR
.PP
.SH DESCRIPTION
\fIi.pca\fR 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.
.SH OPTIONS
.SS Parameters:
.IP "\fBinput=\fR\fIname,name\fR[\fI,name,name\fR,...] 
.br
Name of two or more input raster map layers.
.IP "\fBoutput=\fR\fIname\fR 
.br
The output raster map layer name to which suffixes are
added.  Each output raster map layer is assigned this
user-specified \fIname\fR with a numerical (.1, .2, ...
.n) suffix.
.IP "\fBrescale=\fR\fImin,max\fR 
.br
The optional output category range. (Default: 0,255) If
rescale=0,0,  no rescaling is performed on output files.
.br
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.
.PP
.SH 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.
.PP
Eigenvalue and eigenvector information is stored in the output maps'
history files. View with \fIr.info\fR.
.SH EXAMPLE
Using the Spearfish Imagery sample dataset
\fC
.DS
.br
i.pca in=spot.ms.1,spot.ms.2,spot.ms.3 out=spot_pca
.br
.br
r.info -h spot_pca.1
.br
   Eigen values, (vectors), and [percent importance]:
.br
   PC1   1170.12 ( -0.63 -0.65 -0.43 ) [ 88.07% ]
.br
   PC2    152.49 (  0.23  0.37 -0.90 ) [ 11.48% ]
.br
   PC3      6.01 (  0.75 -0.66 -0.08 ) [  0.45% ]
.br
.DE
\fR
.SH SEE ALSO
Richards, John A.,
\fBRemote Sensing Digital Image Analysis\fR,
Springer-Verlag, 1986.
.PP
Vali, Ali R.,
Personal communication,
Space Research Center, 
University of Texas, Austin, 1990.
.PP
\fI
i.cca
.br
i.class
.br
i.fft
.br
i.ifft
.br
m.eigensystem
.br
r.covar
.br
r.mapcalc
\fR
.SH AUTHOR
David Satnik, GIS Laboratory
.PP
Major modifications for GRASS 4.1 were made by 
.br
Olga Waupotitsch and
Michael Shapiro,
U.S.Army Construction Engineering 
Research Laboratory
.PP
Rewritten for GRASS 6.x and major modifications by 
.br
Brad Douglas
.PP
\fILast changed: $Date: 2011-11-08 12:29:50 +0100 (Tue, 08 Nov 2011) $\fR
.PP
Full index
.PP
© 2003-2011 GRASS Development Team