Why does the undefined “ghost classes” arise during supervised classification in ERDAS IMAGINE?

Question

After a supervised classification in ERDAS IMAGINE I have some undefined "ghost classes" in the attribute table. Also have a look at this youtube video at about 7:12.

I'm wondering why they arise and how they affect the classification result and the accuracy assessment?

My first consideration was that they arise within the merging in the signature Editor. Or is it to pixels, that could not be categorized during the classification process because the possibility of a membership to one ore another class is not high enough?For instance I used the maximum likelihood classifier which categorises the pixels depending on the possibility of being a member of a specific class. In the latter case should they be marked in some way? They would also have to be taken into account in the accuracy assessment because the pixels are still there but were not assigned to a class.

Answer 1

As best I can tell it would make no difference aside from annoyance as the table indicates 0 pixels are classified with these "ghost values". Hence the term. It appears that the pixel values are continuous so if you leave a gap --say a class for 3 and a class for 5-- then it will fill the gap with zeros in the table. In this example class 4.