The digital image process, which involves algorithms that examine the unknown pixels in the image and aggregate them into a number of classes based on the natural groupings or cluster present in the image is known as -

Image classification:

The overall objective of image classification is to automatically categorize all pixels in an image into land cover classes or themes. It can be classified into two, 

1. Unsupervised classification
2. Supervised classification

Unsupervised classification:

• Unsupervised classifiers do not utilize training data as the basis for classification.
• Rather, this family of classifiers involves algorithms that examine the unknown pixels in an image and aggregate them into a number of classes based on the natural groupings or clusters present in the image values.
• It performs very well in cases where the values within a given cover type are close together in the measurement space, data in different classes are comparatively well separated

Hence, option 3 is the correct answer.

Supervised classification:

• Supervised classification can be defined normally as the process of samples of known identity to classify pixels of unknown identity.
• Samples of known identity are those pixels located within training areas.
• Pixels located within these areas term the training samples used to guide the classification algorithm to assigning specific spectral values to appropriate informational class