Also, in my specific case, I would have to avoid adding other classes. To clarify why it should be like so: my study area is a mountainuous area in North-West Italy. I masked features such as water bodies, snow, glaciers, vegetation, and so on, to obtain a map of bare soil only. Now, in this unmasked area, some rock glaciers (i.e. glaciers that are "buried" in the ground) are present. I have their locations in form of polygons, and now I want to use these outlines as my region of interests (ROIs) for the Supervised Classification with i.maxlik on the RGB composed by 1) the elvation map, 2) the band4 of Landsat ETM+, and 3) the cumulative solar radiation map. The aim is to classify the pixels in the unmasked area out of the rock glacier outlines which are similar to the rock glaciers with the maximum likelihood algorithm, according to the three mentioned RGB band values. *As I don't know what is around the rock glaciers, I am not able to specify other classes unfortunately.* So, is there a way I can do a Supervised with only one class in GRASS? Would you suggest another software (better if open source)?
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