[R-sig-Geo] image classification in R

[Edzer Pebesma]

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-bounces at stat.math.ethz.ch on behalf of Edzer Pebesma
> Sent: Fri 4/17/2009 5:32 PM
> To: Corey Sparks
> Cc: r-sig-geo at 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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>>     
>
>   
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-- 
Edzer Pebesma
Institute for Geoinformatics (ifgi), University of Münster
Weseler Straße 253, 48151 Münster, Germany. Phone: +49 251
8333081, Fax: +49 251 8339763 http://ifgi.uni-muenster.de/
http://www.springer.com/978-0-387-78170-9 e.pebesma at wwu.de