The Minimum Distance to Mean Classification

This is a simple and commonly used classification algorithm and the classification accuracies are comparable to any other classification. The user in this classification has to provide the mean vectors for each class in each band from the training sets. In this classification distance of each mean vector is calculated for each unknown pixel this distance is calculated using Euchdian distance based on 

Pythagorean theorem or around the block distance measures. The computation of the Euclidian distance from point a (jc,y) to the mean of class 1 measured in 

Each unknown pixel is then placed in the class closest to the mean vector in this band space. For this classified image there were 16 gray levels, each representing a class, to which a color is assigned. This minimum distance classification has all seven TM bands, including the thermal (Fig. 17). 

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