Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification

The maximum-likelihood classification of remotely sensed data involves considerable computational effort, in the process calculating a large amount of information on the class membership characteristics for each case (e.g., pixel). Little of this information, however, is made available in the conventional output, which consists simply of the most likely class of membership for each case. More of the information generated in the classification can be output, specifically the a posteriori probabilities and typicalities of class membership.