[GRASS-user] Creating PCA Plot

[Rashad M]

Thanks Nikos Alexandris,

This is helpful for me. So to get a pca plot i need to use just d.correlate
with the output raster from i.pca right?

Also i.pca changes spatial position and to output of i.pca will have the
changed pixel position




On Wed, Feb 6, 2013 at 2:33 PM, Nikos Alexandris <nik at nikosalexandris.net>wrote:

> Rashad M wrote:
>
> > Hi,
>
> Hi Rashad :-)
>
> > How to create a PCA plot for two channels of a landsat image.?
>
> In grass use d.correlate, in R too many options!
>
> > i.pca outputs a eigen values, vectors and percentage importance
>
> Quoting myself :-p:
>
> --%<---
> The eigenvalues define proportionally the length of the axes of variation
> and the eigen or characteristic vectors define the direction of the
> variation (Ahearn and Wee, 1991). Since both the eigenvectors and the PCs
> only define directions, they can be arbitrarily multiplied by −1
> (Cadima and Jolliffe, 2009).
> --->%--
>
> Effectively, percentages indicate the amount of variance that has been
> redistributed in a Principal Component -- remember, PCs are sorted from
> the one that holds the largest variance to the one that holds the smallest
> variance.
>
> > Could anybody explain how to plot it?
>
> You mean just a bi-variate scatter-plot?
>
> PCA is a linear transformation for multivariate data sets. The new,
> transformed variables (or dimensions or channels or you name them) can
> then be plotted the same way as any other raster map. E.g., d.histogram
> for single stuff, d.correlate (for a scatter-plot) and probably more.
>
>
> > Does i.pca transforms/changes pixel values?
>
> Yes.
>
> Normally, one would select Landsat bands of interest, i.e. bands that are
> profiling "wanted" landscape features. A PCA would then transform a set of
> bands into something new: sorted variables in which the original variance
> of the data is redistributed in a way that the first Principal Components
> contain most of it, while the higher order transformed variables contain
> the smallest amounts of the original variance.  Note, changes tend to
> appear in some of the higher order PCs.  Noise, is most of the time
> accumulated in the last PC. And, of course, there are many and diverse
> uses of PCs (like compression, fusion, etc.).
>
> (So,) If you have your PCs of interest, then you can scatter-plot them in
> grass with <d.correlate> for example.  In R, however, you can load, in
> theory, infinite number of dimensions (in your wording == channels) and
> plot really nice and fancy stuff.
>
> I have tried to clearly present the PCA concept in my work.  Will send you
> a link and stuff of mine -- they might be useful for you to make them even
> better (!).
>
> Additionally, recently I have seen some very nice tri-variate PC plots in
> some presentation... (dunno remember now, it was certainly someone inside
> the GRASS GIS community!).
>
> Ah, don't forget to have a look in GRASS-Wiki (and maybe help iron the
> page!): <http://grasswiki.osgeo.org/wiki/Principal_Components_Analysis>.
>
> Best, N
>
>


-- 
Regards,
   Rashad
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