[GRASS-user] R: Re: Performing a Maximum Likelihood Supervised classification with a single band

[Nikos Alexandris]

Umberto Filippo Minora wrote:

> > 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.


Moritz Lennert:
 
> 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

Interesting.  But that would depend a lot of how big the study extent
is, how many the training areas are, how they are spread, the illumination
geometry of the input acquisition, and more I guess.


> 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...

I second that.  You could also try some PCA to see if you can get
some enhanced "spectral profiles" (albeit the output range of values
will be something else than the input of course) for what you are looking for.
Just feed as many bands in i.pca and see for interesting principal components.

Nikos