Maximum Likelihood (2) — when it applies.

• The most commonly used decision rule in supervised classification.
• Assumptions:
  • Prior probabilities are equal for all classes (drop this → Bayesian rule, slide 15).
  • Each input band has a normal distribution inside each class.
• Works well when classes are well-sampled and spectrally distinct; struggles on rare classes or non-Gaussian distributions.