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**Essay — Unsupervised classification: details, advantages, disadvantages.** **How it works** 1. Choose the number of clusters (e.g., **ISODATA** or **K-means**). 2. Algorithm iterates: assign pixels to nearest cluster centroid → recompute centroids → repeat until convergence. 3. Analyst **labels** each cluster post-hoc by comparing to reference data. **Advantages** - **No training data required** — fast first pass for unfamiliar scenes. - **Reveals natural spectral groupings** the analyst might not have predicted. - Useful as a **preprocessing** step for supervised classification or for stratifying sampling. **Disadvantages** - Clusters don't always map cleanly to meaningful land-cover classes — one class may split across clusters, or a single cluster may contain several classes. - Sensitive to **K** and initial seeds; different runs can give different results. - Post-classification **labeling and merging** (often via **Recode**) is still required.

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