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2

In your case you could also use the raster calculator directly, because you have constant intervals Int("your_raster"/200) then you can use raster to polygon


2

I think an easier way to do this would be to use the Reclassify tool. You'll need the Spatial Analyst Extension. Then, you just use the Raster to Polygon tool. If you don't have spatial analyst, there are open source options. Just search this site for reclassify.


4

I would look at the support of you individual classes. If support for a given class is marginal in your fit model, the error may propagate in very undesirable ways. I would also consider fitting a series of binary models and predicting probabilities of each class separately. You could then perform a sensitivity test on the probabilities and evaluate if ...


4

It is a difficult thing that you are attempting. Small subtle changes in reflectances caused by different acquisition dates will cause major errors to arise when using your approach. You will have to do more preprocessing of your data, in order to have your approach be reliable. Normalizing the other years to your reference will most likely help, but it may ...


0

As @xunilk suggested, I found GDAL to be the tool I was looking for. I also used Counter to create a form of histogram that helped me complete my analysis. for i in xrange(0,iRange,resampleInterval): for j in xrange(0,jRange,resampleInterval): scanline = band.ReadRaster(i, j, windowSize, windowSize, windowSize, windowSize, band.DataType) ...



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