Thank you very much for your detailed answer that made me understand a lot,
and also it pointed out what I was thinking: R does not use the spatial
information for classification.

The image (for the moment) is rather small, as it is a sample of 512x512
pixels. I have to compare the effect of a segmentation method over raw data
for various unsupervised techniques.
My idea was to do the classification in R, because it handles many more
methods then GIS/RS software I have available.

I will investigate some of the points raised and in case I will come back
with more clear ideas and questions.

Thank you very much to everybody for the support.

Laura



2008/6/12 Agustin Lobo <Agustin.Lobo@ija.csic.es>:

> If your images are large (and images typically are large because pixel size
> has to be small compared to the extent of the image for the image to
> be of acceptable quality for our vision system), I do not advice you
> to get them into R for processing as R has severe memory limits
> and many classification techniques are not precisely memory-efficient
> (but see clara() in package cluster, actually read
> http://cran.r-project.org/web/views/Cluster.html).
>
> I think that you should sample your image in a RS/GIS environment making
> sure you cover all
> the radiometric space and import only a table pixels x bands into R, the
> actual nb. of pixels depending on your HW/SW configuration (but 10000 would
> be a good start). Then use the numerous R classification tools to define the
> centroids and once you have them use again your RS/GIS program to actually
> assign each pixel in the image to a centroid according to a given rule (i.e.
> maximum likelihood). There might be
> ways of writing an efficient assignation step within R itself also, I think
> that mclust package does it.
>
> Another way of reducing the number of individuals to classify is performing
> a segmentation of the image first and then classify segments
> instead of pixels (i.e.
> # Lobo, A. 1997.  Image segmentation and discriminant analysis for the
> identification of land cover units in Ecology. IEEE Transactions on
> Geoscience and Remote Sensing, 35(5): 1- 11.
> http://wija.ija.csic.es/gt/obster/ABSTRACTS/alobo_ieee97.pdf
> perhaps other articles in
> http://wija.ija.csic.es/gt/obster/alobo_publis.html
> might be of help)
>
> In any case, note that img in your code should be converted into
> a multivariate table pixels x bands for most classification
> functions in R to work. Note that this fact makes obvious
> that classification approaches to image processing do not make
> use of the spatial information of the image, which is actually
> a fundamental part of the information of any image.
>
> Agus
>
> Laura Poggio escribió:
>
>> Dear list,
>> I am trying to do some clustering on images. And I have two main problems:
>>
>> 1) Clustering multiband images.
>> I managed to be successful with a single band image, but when trying to
>> apply to a 3 band I get the following warning message:
>> In as.matrix.SpatialGridDataFrame(x) :
>>  as.matrix.SpatialPixelsDataFrame uses first column;
>>  pass subset or [] for other columns
>>
>>
>> 2) saving clustering results as grid or image.
>> I get a vector of clusters, but without both coordinates. How it is
>> possible
>> to transform it in a grid?
>>
>> Here the code I use to read the image itself and to do the clustering:
>>
>> library(rgdal)
>> fld <- system.file("E:/data/IMG/fr/", package="rgdal")
>> img <- readGDAL("123_rawR.tif")
>>
>> kl <- kmeans(img, 5)
>>
>> I am quite new to image processing, especially within R, and any help is
>> greatly appreciated.
>>
>> Thank you in advance
>>
>> LP
>>
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>>
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>>
>>
> --
> Dr. Agustin Lobo
> Institut de Ciencies de la Terra "Jaume Almera" (CSIC)
> LLuis Sole Sabaris s/n
> 08028 Barcelona
> Spain
> Tel. 34 934095410
> Fax. 34 934110012
> email: Agustin.Lobo@ija.csic.es
> http://www.ija.csic.es/gt/obster
>

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