What does 'unsupervised classification' refer to in remote sensing?

Unsupervised classification in remote sensing is a technique that involves categorizing pixels into various classes based solely on their spectral signatures without the need for prior training data. This method is particularly beneficial when the analyst does not have labeled examples of the classes that exist within the image data. The classification process relies on algorithms that identify patterns and similarities among the pixels, leading to the grouping of similar spectral responses into clusters.

This allows for the identification of distinct types of land cover or features based on the inherent characteristics of the data itself, rather than relying on external training data or human interpretation. This aspect makes unsupervised classification a powerful tool, especially in exploratory analyses where the exact nature of the classes is unknown.

In contrast, other classification methods typically involve prior knowledge or labeled data, which is not the case for unsupervised classification, emphasizing the uniqueness of this approach.