"Unsupervised" Image Classification

The classification of the pixels that compose a remotely-sensed raster image into land use/land cover categories- such as forest/trees, grass, buildings/roads, etc.- is primarily accomplished via "supervised" or "unsupervised" classification processes.  "Supervised" classification involves the creation of categories of land use/cover, and subsequent assignment of image pixels to each category, mainly by way of manual determination by an image analyst.  "Unsupervised" classification, on the other hand, begins with a computer algorithm assignment of image pixels into a pre-defined number of class types, and a subsequent determination by an analyst what each class represents and whether the computer-assigned classes for the pixels are accurate.  

The above image includes five feature class types, with each pixel assigned to each class by the computer algorithm, which clusters pixels according to their number values in each band layer.  The algorithm initially produced 50 different classes, which were then manually condensed into the five seen above.  The total surface area for each class was then calculated, and the impermeable and permeable surface areas as well.