Essay — Unsupervised classification: details, advantages, disadvantages.
How it works
- Choose the number of clusters (e.g., ISODATA or K-means).
- Algorithm iterates: assign pixels to nearest cluster centroid → recompute centroids →
repeat until convergence.
- 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.