MODIFIED HIGHEST CONFIDENCE FIRST CLASSIFICATION

A classification procedure is developed that distinguishes between pix els that are clearly associated with a given class versus those where class assignment is uncertain. Alternatively, most commonly used class ification algorithms force each pixel into a single class without rega rd to certainty. By classifying pixels in order of certainty and consi dering spatial context, pixels with weak observational evidence for cl assification are prevented from contributing to their neighbor's decis ions. Subsequently, a better decision is made for the uncertain pixels by considering the previously classified neighbors. Degrees of certai nty measures can assist in later map accuracy assessment by allowing f or stratified sampling nf zones having similar certainty levels.