A decision rule is the mathematical test a supervised classifier uses to assign each unknown pixel to one of the training classes. The most common rules:

• Minimum Distance — distance to each class mean; pick the closest.
• Mahalanobis Distance — like Minimum Distance but weighted by class covariance.
• Maximum Likelihood — pick the class with the highest probability assuming Gaussian class distributions.
• Parallelepiped — box-shaped decision regions set by min/max in each band; fast but overlaps and gaps.