mc
short answer
essay
Prompt
[MANDATORY] Supervised classification — details, advantages, disadvantages.
Plain English answer (default view) — what you'd actually write on the test
**Supervised classification** — you teach the computer what each class looks like, then it sorts every pixel. ### How it works 1. **Find places you know** — forest patches, water bodies, urban blocks. 2. **Sample those** — give the computer color signatures of each class. 3. **Pick a decision rule** — Max Likelihood is the most common. 4. **Run it** — every pixel in the scene gets a class label. 5. **Check accuracy** — compare to ground-truth points you didn't use for training (confusion matrix, kappa). ### Strengths - **Higher accuracy than unsupervised** when your training data is good. - **You control the class scheme** — useful when you have a specific question. - **Works well** when classes are spectrally distinct. ### Weaknesses - **Training-sample collection is slow** — fieldwork, photo interpretation, ground truth. - **Bias risk** — if you forget a class, the computer can't predict it. - **Max Likelihood assumes Gaussian classes** — fails on weird-shaped distributions.
🔬 Technical version / model bullets (revealed on click) — one bullet per line
**Procedure** 1. Define training sites (AOIs) for each known class. 2. Compute class signatures (mean + covariance per band). 3. Apply a decision rule — Min Distance, Mahalanobis, **Max Likelihood**, Parallelepiped — to every pixel. 4. Accuracy assessment (confusion matrix, kappa). **Advantages** Analyst controls the class scheme — matches the question. Higher accuracy than unsupervised when training data is good. Works well when classes are spectrally distinct + well-sampled. **Disadvantages** Labor-intensive — good ground truth takes time. Bias risk — unrepresented classes won't be predicted. Max Likelihood assumes multivariate-normal class distributions.
💡 Mnemonic / memory aid (shown on hover)
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