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

[Umberto Filippo Minora]

Thank you Moritz,

I am aware that the combination of different bands might lead to a significant difference, not detectable comparing them one against one at a time. I think this can be the case where two or more bands are correlated to each other for specific sites (eg. my training areas), but I am not an expert in statistics so please forgive me if I am wrong. However, I performed a PCA and I saw that this might not be the case. Moreover, I masked all the features in my image other than "soil" (I masked clouds, snow, water, ice, etc.) to exclude them from the classification. Indeed, my scope is to use the reflectance of the rock glacier areas (my training areas) to find similar values as a proxy of permafrost presence over the unmasked pixels of my image.
In the light of such, I think I would rather use "r.texture" as Veronica suggested to perform "i.maxlik", OR your suggestion about using the statistics of the reflectance pixels in band 4 in my training areas with r.recode.
Thanks for all for your useful suggestions, which brought me to a next step of my work!

Il 14/10/15 14:54, Moritz Lennert  <mlennert a club.worldonline.be> ha scritto: 
> 
> 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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