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short answer
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Prompt
[MANDATORY] Unsupervised classification — details, advantages, disadvantages.
Plain English answer (default view) — what you'd actually write on the test
**Unsupervised classification** — let the computer find natural groupings, label them after. ### How it works 1. **Tell the computer how many clusters to look for** (say, 30). 2. **ISODATA (or K-means)** iteratively groups similar pixels. 3. **You inspect the result** and label each cluster — *"cluster 7 looks like wetland."* 4. **Often merge** redundant clusters with the **Recode** tool. ### Strengths - **No training data required** — fast first look at unfamiliar imagery. - **Reveals natural groupings** the analyst didn't predict. - **Useful as a starter** before doing supervised classification, or for stratified sampling. ### Weaknesses - **Clusters don't always match meaningful categories** — one class may split across multiple clusters, one cluster may contain several classes. - **Sensitive to K and initial seeds** — different runs give different results. - **Post-classification cleanup is still required** — labeling, merging, re-running with different K.
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**Procedure (ISODATA / K-means)** 1. Choose number of clusters. 2. Iterate: assign pixels to nearest centroid → recompute centroids → repeat until convergence. 3. Analyst labels each cluster post-hoc from reference data. **Advantages** No training data required — fast first pass. Reveals natural spectral groupings the analyst might not have predicted. Useful as preprocessing for supervised classification or for stratified sampling. **Disadvantages** Clusters don't always map to meaningful classes (one class may split; one cluster may contain several classes). Sensitive to K and initial seeds — different runs give different results. Post-classification labeling/merging (often via Recode) still required.
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