clustering multi band images

thank you. It seems now solved!

Laura

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

Laura Poggio escribi?:
.../...

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

May I just ask you if you have easily available an example of code to
transform the image in a multivariate table pixels x bands? This would be
helpful to avoid many trial and errors (especially the second one). Sorry
for that but at my institution I am the only one dealing with R...

Thank you again

Laura

You already did it in:
kl <- kmeans(as(img, "data.frame"), 5)

Perhaps you want to do it in 2 steps:

imgtabla <- as(img, "data.frame")
kl <- kmeans(imgtabla, 5)

You can look at the first rows of imgtabla with

imgtabla[1:5,]
or
head(imgtabla)
and then
dim(imgtabla)
summary(imgtabla)

If img were your complete Landsat image, the same steps
would yield an imgtabla with 262144 x 6 (as I assume you are not
using the thermal band). In that case, you probably want
to run PCA and use only the first 3 PCs for classification, as they
typically
account for >95% of the total variance and you cam always apply
the inverse transform to the centroids to recover the original
metric.

As a matter of fact, I think it's more practical
here to convert imgtabla from data.frame to matrix, as all values are
numerical here.

Agus

   Agus


       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>
       <mailto: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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--
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 at ija.csic.es
http://www.ija.csic.es/gt/obster

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