Hi list! I am searching for an OSGeo implementation (at the very least simply FOSS will do) of the ISODATA clustering algorithm [*]. I want to avoid using a commercial tool. Currently I have reviewed the following "options":
GRASS-GIS GRASS has a modified k-means implementation in its <i.cluster> module [1]. However, it requires at least two raster maps to run. The ISODATA algorithm is known to run even on a single raster map. OpenEV The "old" (but very good) OpenEV (is convenient since it reads directly grass raster maps and) has an integrated ISODATA-based classification tool [2]. While testing with single Landsat bands ranging [0,255] and and NDVI image [-0.1,1.0], it get stuck somehow after some iterations complaining bout "ZeroDivisionError: float division". Don't know if there any restrictions concerning the input data format. OrfeoToolBox OrfeoToolBox has also a clustering algorithm [3] but I am unsure that it is an implementation of the ISODATA. Can't trace a clean documentation about it. It seems as yet another k-means based algorithm. Fast ISODATA implementation (by David Mount) (Not tested yet!) Any other candidates? Warmest regards, Nikos -- [*] ISODATA: a) <http://www.dtic.mil/cgi- bin/GetTRDoc?Location=U2&doc=GetTRDoc.pdf&AD=AD0699616> b) <http://en.wikipedia.org/wiki/Multispectral_pattern_recognition#ISODATA_method> [1] <http://grass.osgeo.org/grass64/manuals/i.cluster.html> [2] <http://openev.sourceforge.net/> [ Sometimes it fails giving following message: --%<--- Traceback (most recent call last): File "/geo/osgeo/binaries/FWTools-2.0.6/lib/python2.2/site-packages/gtk.py", line 125, in __call__ ret = apply(self.func, a) File "./../tools/isodata.py", line 529, in classify_cb d /= d_tresh ZeroDivisionError: float division --->%-- ] [3] Mean-shift clustering: <http://orfeo- toolbox.org/CookBook/CookBooksu17.html#x31-430002.4.4> and <http://gracilis.carleton.ca/CUOSGwiki/index.php/Image_Classification_Tutorial_using_Orfeo_Toolbox#Clustering> [4] <http://www.cs.umd.edu/~mount/Projects/ISODATA/>
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