On 14/10/15 14:20, Umberto Filippo Minora wrote:
Thanks Nikos,
Well, actually my training areas are defined as they need to be specific
features in my location (rock glaciers). I would like to use i.maxlik to
classify the other pixels of my image (other than rock glaciers, alias
my training areas). They should be classified as "similar" to my
training areas with a certain degree (a certain likelihood). The fact I
am only using one band is driven by the fact that only band 4 of Landsat
7 reflectances shows significant difference in value in my training
areas than the other unclassified areas. Using twice the same band (band
4) is fine, but I am having difficulties in grouping the same band.
Using i.group for instance recognizes that the map is the same and does
not add it twice to the group, therefore I cannot use i.maxlik.
I don't know if producing textures is the way to go in my case, but I
will give this a try before rejecting this option. First, however, I
need to study this function output.
Meanwhile, many thanks to both of you (Veronoca and Nikos)! If you have
any other useful idea, I will consider them as well.
If you only work with one band, you could use your training areas to
define the mean value and standard deviation in band 4 that corresponds
to your class of interest and then just use r.recode to classify your
image with something like this:
mean-stddev:mean+stddev:1
*:0
Obviously you can play around with the stddev value and see if it should
be 1 stddev, 1/2 stddev, 2 stddev, etc, depending on your desired
confidence level. Obviously this all only holds if your distribution is
normal, but then again AFAIU that's the basic underpinning if maximum
likelihood.
However, even though you might not observe significant differences in
values between sites in individual bands, you might have significant
differences in the combination of bands. With that in mind, it still
might be worthwile to try your classification with several bands. At
least bands 2,3 and 5 might be interesting to add as they are used in
ice and snow indices...
Moritz
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