Maximum Likelihood Classification (MLC) assumes each class’s pixel values follow a multivariate Gaussian distribution. For each unknown pixel, it computes the probability of membership in every class (from the class mean and covariance matrix) and assigns the pixel to the class with the highest probability.

• Strength: statistically principled; handles correlated bands well.
• Weakness: requires enough training samples to estimate covariance reliably; struggles when classes aren’t Gaussian.