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@@ -12,41 +12,23 @@ reading the input raster map layers. When the rescale
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option is used, the output files are rescaled to fit the
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min,max range.
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-<h2>OPTIONS</h2>
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-
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-<h3>Parameters:</h3>
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-<dl>
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-<dt><b>input=</b><em>name,name</em>[<em>,name,name</em>,...]
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-<dd>Name of two or more input raster map layers.
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-
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-<dt><b>output=</b><em>name</em>
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-<dd>The output raster map layer name to which suffixes are
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-added. Each output raster map layer is assigned this
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-user-specified <em>name</em> with a numerical (.1, .2, ...
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-.n) suffix.
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-
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-<dt><b>rescale=</b><em>min,max</em>
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-<dd>The optional output category range. (Default: 0,255) If
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-rescale=0,0, no rescaling is performed on output files.
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-
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-<dd>If output is rescaled, the output raster will be of type CELL. If
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-the output is not rescaled, the output raster will be of type DCELL.
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+<p>
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+If the output is not rescaled (<em>rescale=0,0</em>, the output raster
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+maps will be of type DCELL, otherwise the output raster maps will be of
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+type CELL.
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-<dt><b>-n</b> <em>normalize input raster maps</em>
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-<dd>By default, the values of the input raster maps are centered for each
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+<p>By default, the values of the input raster maps are centered for each
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map separately with <em>x - mean</em>. With <em>-n</em>, the input raster
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maps are normalized for each map separately with <em>(x - mean) / stddev</em>.
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Normalizing is highly recommended when the input raster maps have different
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units, e.g. represent different environmental parameters.
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-<dt><b>-f</b> <em>output will be filtered input bands</em>
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-<dd>This option, together with the <em>percent</em> option, can be used
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-to remove noise from input bands. Input bands will be recalculated from
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-a subset of the principal components. The subset is selected by using
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-only the most important (highest eigenvalue) principal components which
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-explain together <em>percent</em> percent variance observed in the
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-input bands.
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-</dl>
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+<p>The <em>-f</em> flag, together with the <em>percent</em> option, can
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+be used to remove noise from input bands. Input bands will be
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+recalculated from a subset of the principal components (inverse PCA).
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+The subset is selected by using only the most important (highest
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+eigenvalue) principal components which explain together <em>percent</em>
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+percent variance observed in the input bands.
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<h2>NOTES</h2>
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@@ -60,16 +42,20 @@ history files. View with <em>r.info</em>.
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<h2>EXAMPLE</h2>
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-Using the Spearfish Imagery sample dataset
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+Using Landsat imagery in the North Carolina sample dataset:
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<div class="code"><pre>
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-i.pca in=spot.ms.1,spot.ms.2,spot.ms.3 out=spot_pca
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+i.pca in=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,lsat7_2002_70 \
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+ out=lsat7_2002_pca
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-r.info -h spot_pca.1
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+r.info -h lsat7_2002_pca.1
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Eigen values, (vectors), and [percent importance]:
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- PC1 1159.75 ( 0.6237, 0.6503, 0.4337) [88.38%]
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- PC2 146.50 ( 0.2403, 0.3685,-0.8981) [11.16%]
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- PC3 5.97 (-0.7438, 0.6643, 0.0735) [ 0.45%]
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+ PC1 4334.35 ( 0.2824, 0.3342, 0.5092,-0.0087, 0.5264, 0.5217) [83.04%]
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+ PC2 588.31 ( 0.2541, 0.1885, 0.2923,-0.7428,-0.5110,-0.0403) [11.27%]
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+ PC3 239.22 ( 0.3801, 0.3819, 0.2681, 0.6238,-0.4000,-0.2980) [ 4.58%]
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+ PC4 32.85 ( 0.1752,-0.0191,-0.4053, 0.1593,-0.4435, 0.7632) [ 0.63%]
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+ PC5 20.73 (-0.6170,-0.2514, 0.6059, 0.1734,-0.3235, 0.2330) [ 0.40%]
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+ PC6 4.08 (-0.5475, 0.8021,-0.2282,-0.0607,-0.0208, 0.0252) [ 0.08%]
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</pre></div>
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