What does unsupervised classification do in image processing?

Unsupervised classification plays a crucial role in image processing by automatically grouping pixels based on their intrinsic characteristics, such as spectral properties. This process involves analyzing the data without any prior information or labeled training data. The algorithm identifies patterns and natural clusters within the data, allowing similar pixels to be categorized together based on their features.

For example, in remote sensing, unsupervised classification can help in distinguishing different types of land cover by examining the unique spectral signatures of various materials. This method is particularly useful when the researcher does not have specific labels for the different classes present in the image but instead seeks to uncover the underlying structure of the data.

The other options reference concepts that aren't aligned with the nature of unsupervised classification. Prior information on data and labeled examples for training are integral to supervised classification methods, where known data is used to inform the classification process. Eliminating noise from images pertains more to image preprocessing techniques rather than classification itself.