Classification of images provides useful information about land cover, based on the spectral radiometric response of the cover type.This is done by one of two basic methods: unsupervised or supervised classification. Unsupervised classification categorizes continuous raster data into discrete thematic groups having similar spectral-radiometric values. Supervised classification allows the analyst to define classes of interest. The computer then calculates training statistics based on the definitions found in signature files and assigns each pixel of the image to the class that it most closely resembles.
For unsupervised training and classification, ERDAS Imagine employs the (ISODATA) clustering technique which uses the statistics of the data to evaluate the similarities or differences of the pixel values then groups the pixels into separate classes. This process takes several passes, or iterations, until it reaches a convergence threshold. The groups are then defined by a signature file, which can be used to create a new raster layer of discrete class values.
For more, please download the attached tutorial manual.