Essay — Supervised classification: details, advantages, disadvantages.

How it works

1. Define training sites (AOIs) for each known class.
2. Compute class signatures (mean + covariance per band).
3. Apply a decision rule — Minimum Distance, Mahalanobis, Maximum Likelihood, Parallelepiped — to every pixel.
4. Evaluate with an accuracy assessment (confusion matrix, kappa).

Advantages

• You control the class scheme — matches the analyst’s question.
• Higher accuracy than unsupervised when training data is good.
• Works well when classes are spectrally distinct and well-sampled.

Disadvantages

• Labor-intensive — collecting good ground truth takes time.
• Bias risk — classes not represented in training won’t be predicted.
• Assumes training statistics generalize across the scene (fails on mixed pixels or rare classes).
• Maximum Likelihood requires multivariate-normal class distributions.