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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.

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