I'd like to perform a supervised classification using the Semi-Automatic Classification Plugin and a maximum likelihood classification algorithm. I read about a rule of thumb which says "if training data are being extracted from n bands, then a minimum of >10n pixels of training data are collected for each class" (Jensen). I'm using a Landsat image with six bands, so I assume that I have to select at least 60 training pixel per class. Somehow, I'm confused because I also heard people saying that you need 10times training pixels as you have classes.
Is there a general rule of thumb for selecting ROIs? Do you count the training pixels by drawing the polygons around them?