Classified data often manifest a salt-and-pepper appearance due to the inherent spectral variability encountered by a classification when applied on a pixel-by pixel basis (Lillesand and Kiefer, 1994). It is often desirable to “smooth” the classified output to show only the dominant (presumably correct) classification.
Ground truth or field survey is done in order to observe and collect information about the actual condition on the ground at a test site and determine the relationship between remotely sensed data and the object to be observed. It is recommended to have a ground truth at the same time of data acquisition, or at least within the time that the environmental condition does not change.
Classification accuracy assessment is a general term for comparing the classification to geographical data that are assumed to be true to determine the accuracy of the classification process. Usually, the assumed true data are derived from ground truth. It is usually not practical to ground truth or otherwise test every pixel of a classified image. Therefore a set of reference pixels is usually used. Reference pixels are points on the classified image for which actual data are(will be) known. The reference pixel are randomly select.(Congalton , 1991)