clustering multi band images

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.

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 at ija.csic.es 
<mailto:Agustin.Lobo at 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 escribi?:

        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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