The four ways the computer assigns a pixel to a class, ranked by sophistication:

• 📦 Parallelepiped
  • How: box-shaped regions defined by each class’s min/max in every band
  • Pro: simple, fast first pass
  • Con: leaves gaps + has corner overlap problems
• 📏 Minimum Distance
  • How: assign to whichever class mean is closest (Euclidean)
  • Pro: no unclassified pixels, very fast
  • Con: ignores class shape, force-fits outliers
• 📐 Mahalanobis Distance
  • How: Min Distance scaled by each class’s covariance
  • Pro: accounts for class shape (ellipsoidal)
  • Con: still assumes equal priors
• 🎯 Maximum Likelihood
  • How: pick the most probable class, assuming Gaussian distributions
  • Pro: most accurate when classes are well-sampled
  • Con: needs lots of training data, fails on non-Gaussian classes