In this tutorial, i will present how to do supervised classification in Erdas Imagine
Supervised classification uses image pixels representing regions of known, homogenous surface composition ‘training areas’ to classify unknown pixels.

A Supervised Classification describes information about the data of land use as well as land cover for any region. Soil type, Vegetation, Water bodies, Cultivation, etc.
Supervised training is closely controlled by the analyst. In this process, you select pixels that represent patterns or land cover features that you recognize, or that you can identify with help from other sources, such as aerial photos, ground truth data, or maps. Knowledge of the data, and of the classes desired, is required before classification.

By identifying patterns, you can instruct the computer system to identify pixels with similar characteristics. If the classification is accurate, the resulting classes represent the categories within the data that you originally identified.
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