Question

Unsupervised classification identifies larger number of spectrally-distinct classes than supervised classification. True

Answer 1

In case of unsupervised classification , algorithms are used by various computers which is used to join various pixels together based on their similarity.

This classification is also called the clustering and thus use this kind of classification.

This also does not need to have knowledge.

In the supervised classification human error almost occurs less.

Answer 2

Unsupervised classification algorithms require the analyst to assign labels and combine classes after the fact into useful information classes (e.g. forest, agricultural, water, etc). In many cases, this after the fact assignment of spectral clusters is difficult or not possible because these clusters contain assemblages of mixed land cover types. Generally speaking, unsupervised classification is useful for quickly assigning labels to uncomplicated, broad land cover classes such as water, vegetation/non-vegetation, forested/non-forested, etc). Furthermore, unsupervised classification may reduce analyst bias.