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**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.

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