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 >> >> _______________________________________________ >> R-sig-Geo mailing list >> R-sig-Geo@stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/r-sig-geo >> > > > ------------------------------------------------------------------------ > > _______________________________________________ > R-sig-Geo mailing list > R-sig-Geo@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/r-sig-geo > -- 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.pebe...@wwu.de _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo
