Two major categories of image classification techniques include unsupervised (calculated by software) and supervised (human-guided) classification.

Unsupervised classification is where the outcomes (groupings of pixels with common characteristics) are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and what classes belong together. The user can specify how many times the data are analyzed and the desired number of output classes but otherwise does not intervene in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground (such as wetlands, developed areas, coniferous forests, etc.).

Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these choices as references for the classification of all other pixels in the image. Training areas (also known as testing sets or input classes) are selected based on the knowledge of the user. The user also sets the bounds for how close the matches have to be. These bounds are often set based on the spectral characteristics of the training area, plus or minus a certain increment (often based on "brightness" or strength of reflection in specific spectral bands). The user also designates the outputs (for example, how many final classes are needed).

Many analysts use a combination of supervised and unsupervised classification processes to develop final output analysis and classified maps.