Page "Ground truth" Paragraph 3

Ground truth is important in the initial supervised classification of an image.

When the identity and location of land cover types are known through a combination of field work, maps, and personal experience these areas are known as training sites.

The spectral characteristics of these areas are used to train the remote sensing software using decision rules for classifying the rest of the image.

These decision rules such as Maximum Likelihood Classification, Parallelepiped Classification, and Minimum Distance Classification offer different techniques to classify an image.

Additional ground truth sites allow the remote sensor to establish an error matrix which validates the accuracy of the classification method used.

Different classification methods may have different percentages of error for a given classification project.

It is important that the remote sensor chooses a classification method that works best with the number of classifications used while providing the least amount of error.