In my opinion, and considering that imagery uses to be very large
datasets, unless you want to include spatial characteristics, the best
is to subsample your imagery with your gis, then import the
dataset to R, perform classification with the many tools
available, save the centroids (means, sds, covar matrices depending
on your method) and then allocate pixels to those centroids in your
gis.
Images are too large for R
Agus
Edzer Pebesma wrote:
There's a Task View on clustering, linked from CRAN:
http://cran.r-project.org/web/views/Cluster.html
that will lead you to all types of clustering available, including
hierarchical. I don't know how well it will work for large data sets
such as images, as it calls for constructing n x n distance matrices,
with n the number of pixels.
--
Edzer
Hengl, T. wrote:
Don't forget that you can also use different types of unsupervised classification
methods, such as the fuzzy k-means as implemented in the "kmeans" method.
Here is an example (with landform classes):
http://spatial-analyst.net/wiki/index.php?title=Analysis_of_DEMs_in_R%2BILWIS/SAGA
If you work with large grids, consider also using R+SAGA:
https://stat.ethz.ch/pipermail/r-sig-geo/2009-February/005155.html
T. Hengl
-----Original Message-----
From: r-sig-geo-boun...@stat.math.ethz.ch on behalf of Edzer Pebesma
Sent: Fri 4/17/2009 5:32 PM
To: Corey Sparks
Cc: r-sig-geo@stat.math.ethz.ch
Subject: Re: [R-sig-Geo] image classification in R
Corey,
you can use functions lda or qda (in library MASS) for linear or
quadratic discriminant analysis, respectively, on your training/ground
truth data, and then use the predict method on the resulting objects,
passing the bands (you need to convert the SpatialGridDataFrame to a
data.frame) as newdata to obtain the classified pixels. Make sure that
the band names have identical name in both cases. Then assign the
predicted class to the SpatialGridDataFrame and export.
It has never been clear to me whether "maximum likelihood
classification" in RS refers to lda or qda. Anyway, it's called
discriminant analysis in the statistical literature.
--
Edzer
Corey Sparks wrote:
Dear list,
I want to do some unsupervised image classification of some landsat
imagery, I think I can read in the multi-band rasters using rgdal, but
has anyone tried doing this in R? I am thinking (after looking at
documentation for how GRASS and ArcGIS do it) that I need to do an
initial hierarchical clustering to define clusters, but does anyone
have an idea on how to do a maximum likelihood classification of the
imagery? Would a discriminant function approach work? Any advice
anyone may have would be greatly appreciated, and i'm very sorry but I
don't have a working example yet.
Best
Corey
Corey Sparks
Assistant Professor
Department of Demography and Organization Studies
University of Texas at San Antonio
One UTSA Circle
San Antonio, TX 78249
210 458 6858
corey.sparks 'at' utsa.edu
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