1i.pca(1)                    GRASS GIS User's Manual                   i.pca(1)
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NAME

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

9       imagery, transformation, PCA, principal components analysis
10

SYNOPSIS

12       i.pca
13       i.pca --help
14       i.pca  [-nf]  input=name[,name,...]  output=basename  [rescale=min,max]
15       [percent=integer]    [--overwrite]   [--help]   [--verbose]   [--quiet]
16       [--ui]
17
18   Flags:
19       -n
20           Normalize (center and scale) input maps
21           Default: center only
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23       -f
24           Output will be filtered input bands
25           Apply inverse PCA after PCA
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27       --overwrite
28           Allow output files to overwrite existing files
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30       --help
31           Print usage summary
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33       --verbose
34           Verbose module output
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36       --quiet
37           Quiet module output
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39       --ui
40           Force launching GUI dialog
41
42   Parameters:
43       input=name[,name,...] [required]
44           Name of two or more input raster maps or imagery group
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46       output=basename [required]
47           Name for output basename raster map(s)
48           A numerical suffix will be added for each component map
49
50       rescale=min,max
51           Rescaling range for output maps
52           For no rescaling use 0,0
53           Default: 0,255
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55       percent=integer
56           Cumulative percent importance for filtering
57           Options: 50-99
58           Default: 99
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DESCRIPTION

61       i.pca is an image processing program based on the algorithm provided by
62       Vali (1990), that processes n (n >= 2) input raster  map  layers  and
63       produces n output raster map layers containing the principal components
64       of the input data in decreasing order of  variance  ("contrast").   The
65       output  raster  map  layers are assigned names with .1, .2, ... .n suf‐
66       fixes. The numbers used as suffix correspond to percent importance with
67       .1  being the scores of the principal component with the highest impor‐
68       tance.
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70       The current geographic region definition  and  MASK  settings  are  re‐
71       spected  when reading the input raster map layers. When the rescale op‐
72       tion is used, the output files are rescaled to fit the min,max range.
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74       The order of the input bands does not matter for the  output  maps  (PC
75       scores), but does matter for the vectors (loadings), since each loading
76       refers to a specific input band.
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78       If the output is not rescaled (rescale=0,0, the output raster maps will
79       be  of  type  DCELL,  otherwise  the output raster maps will be of type
80       CELL.
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82       By default, the values of the input raster maps are centered  for  each
83       map  separately  with x - mean. With -n, the input raster maps are nor‐
84       malized for each map separately with (x - mean) / stddev.   Normalizing
85       is  highly recommended when the input raster maps have different units,
86       e.g. represent different environmental parameters.
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88       The -f flag, together with the percent option, can be  used  to  remove
89       noise  from input bands. Input bands will be recalculated from a subset
90       of the principal components (inverse PCA).  The subset is  selected  by
91       using only the most important (highest eigenvalue) principal components
92       which explain together percent percent variance observed in  the  input
93       bands.
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NOTES

96       Richards  (1986)  gives  a good example of the application of principal
97       components analysis (PCA) to a time  series  of  LANDSAT  images  of  a
98       burned region in Australia.
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100       Eigenvalue  and  eigenvector  information is stored in the output maps’
101       history files. View with r.info.
102

EXAMPLE

104       PCA calculation using Landsat7 imagery in  the  North  Carolina  sample
105       dataset:
106       g.region raster=lsat7_2002_10 -p
107       i.pca in=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,lsat7_2002_70 \
108           out=lsat7_2002_pca
109       r.info -h lsat7_2002_pca.1
110          Eigen values, (vectors), and [percent importance]:
111          PC1   4334.35 ( 0.2824, 0.3342, 0.5092,-0.0087, 0.5264, 0.5217) [83.04%]
112          PC2    588.31 ( 0.2541, 0.1885, 0.2923,-0.7428,-0.5110,-0.0403) [11.27%]
113          PC3    239.22 ( 0.3801, 0.3819, 0.2681, 0.6238,-0.4000,-0.2980) [ 4.58%]
114          PC4     32.85 ( 0.1752,-0.0191,-0.4053, 0.1593,-0.4435, 0.7632) [ 0.63%]
115          PC5     20.73 (-0.6170,-0.2514, 0.6059, 0.1734,-0.3235, 0.2330) [ 0.40%]
116          PC6      4.08 (-0.5475, 0.8021,-0.2282,-0.0607,-0.0208, 0.0252) [ 0.08%]
117       d.mon wx0
118       d.rast lsat7_2002_pca.1
119       # ...
120       d.rast lsat7_2002_pca.6
121       In  this  example,  the  first two PCAs (PCA1 and PCA2) already explain
122       94.31% of the variance in the six input channels.
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124       Resulting PCA maps calculated from the Landsat7 imagery (NC, USA)
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SEE ALSO

127       Richards, John A., Remote Sensing Digital Image Analysis, Springer-Ver‐
128       lag, 1986.
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130       Vali, Ali R., Personal communication, Space Research Center, University
131       of Texas, Austin, 1990.
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133        i.cca, g.gui.iclass, i.fft, i.ifft, m.eigensystem, r.covar, r.mapcalc
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135        Principal Components Analysis article (GRASS Wiki)
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AUTHORS

138       David Satnik, GIS Laboratory
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140       Major modifications for GRASS 4.1 were made by
141       Olga Waupotitsch and Michael Shapiro, U.S.Army Construction Engineering
142       Research Laboratory
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144       Rewritten for GRASS 6.x and major modifications by
145       Brad Douglas
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SOURCE CODE

148       Available at: i.pca source code (history)
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150       Accessed: Saturday Jan 21 21:16:51 2023
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152       Main  index | Imagery index | Topics index | Keywords index | Graphical
153       index | Full index
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155       © 2003-2023 GRASS Development Team, GRASS GIS 8.2.1 Reference Manual
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159GRASS 8.2.1                                                           i.pca(1)
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