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i.pca.1grass
Langue: en
Version: 337979 (ubuntu - 24/10/10)
Section: 1 (Commandes utilisateur)
Sommaire
NAME
i.pca - Principal components analysis (pca) program for image processing.KEYWORDS
imagery, image transformation, PCASYNOPSIS
i.pcai.pca help
i.pca input=name[,name,...] output=name [rescale=min,max] [--overwrite] [--verbose] [--quiet]
Flags:
- --overwrite
Allow output files to overwrite existing files- --verbose
Verbose module output- --quiet
Quiet module output
Parameters:
- input=name[,name,...]
Name of two or more input raster maps- output=name
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, ...- 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 dataseti.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 LaboratoryMajor 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: 2009-03-03 00:44:13 +0100 (mar, 03 mar 2009) $
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