- In supervised classification, the images are manually fed and interpreted by the Machine Learning expert to create feature classes.
- In unsupervised classification, the Machine Learning software creates feature classes based on image pixel values.
The choice between supervised and unsupervised classification for image classification depends on several factors including the availability of labeled data, the complexity of the task, and the desired outcome.
- Supervised Classification:
- Pros:
- Requires labeled data, where each image is associated with a correct label, making it suitable when labeled data is available.
- Typically yields higher accuracy since the model learns from explicitly labeled examples.
- Allows for targeted training and fine-tuning of models for specific classes or features.
- Cons:
- Requires a significant amount of labeled data, which can be costly and time-consuming to obtain, especially for large datasets or niche categories.
- May struggle with unseen or ambiguous classes that were not present in the training data.
- Pros:
- Unsupervised Classification:
- Pros:
- Doesn’t require labeled data, making it applicable in scenarios where labeled data is scarce or unavailable.
- Can uncover hidden patterns or structures within the data that might not be immediately apparent.
- Can be more scalable as it doesn’t rely on human annotation efforts.
- Cons:
- Generally, lower accuracy compared to supervised methods as it relies on the inherent structure of the data rather than explicit labels.
- Results may be less interpretable or harder to validate since there are no ground truth labels to evaluate against.
- Might require additional processing or manual intervention to interpret and refine the results.
- Pros:
Justification:
- If there’s a sizable amount of labeled data available and achieving high accuracy is crucial, then supervised classification would be the preferred choice.
- On the other hand, if labeled data is scarce or expensive to obtain, or if there’s a need to explore the data structure without predefined labels, unsupervised classification might be more appropriate.
- Ultimately, the decision should be based on a trade-off between the availability of labeled data, computational resources, desired accuracy, and the complexity of the classification task. There is no one-size-fits-all answer; the choice depends on the specific requirements and constraints of the problem at hand.