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 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: 2011-11-08 03:29:50 -0800 (Tue, 08 Nov 2011) $

       Full index

       © 2003-2013 GRASS Development Team

GRASS 6.4.3                                                                                        i.pca(1grass)