NAME¶

i.pca - Principal components analysis (PCA) for image processing.

KEYWORDS¶

imagery, image transformation, PCA

SYNOPSIS¶

i.pca i.pca help i.pca [-n] input=name[,name,...] output_prefix= string [rescale=min,max] [--verbose] [-- quiet]

Flags:¶

-n

 

Normalize (center and scale) input maps

--verbose

 

Verbose module output

--quiet

 

Quiet module output

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, ...

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 Landsat imagery in the North Carolina sample dataset: g.region rast=lsat7_2002_10 -p i.pca in=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,lsat7_2002_70 \
out=lsat7_2002_pca r.info -h lsat7_2002_pca.1
Eigen values, (vectors), and [percent importance]:
PC1 4334.35 ( 0.2824, 0.3342, 0.5092,-0.0087, 0.5264, 0.5217) [83.04%]
PC2 588.31 ( 0.2541, 0.1885, 0.2923,-0.7428,-0.5110,-0.0403) [11.27%]
PC3 239.22 ( 0.3801, 0.3819, 0.2681, 0.6238,-0.4000,-0.2980) [ 4.58%]
PC4 32.85 ( 0.1752,-0.0191,-0.4053, 0.1593,-0.4435, 0.7632) [ 0.63%]
PC5 20.73 (-0.6170,-0.2514, 0.6059, 0.1734,-0.3235, 0.2330) [ 0.40%]
PC6 4.08 (-0.5475, 0.8021,-0.2282,-0.0607,-0.0208, 0.0252) [ 0.08%]

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 Principal Components Analysis article (GRASS Wiki)

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: 2014-05-15 23:01:15 +0200 (Thu, 15 May 2014) $ Full index © 2003-2014 GRASS Development Team