The Effect of Prior Probabilities in the Maximum Likelihood Classification on Individual Classes: A Theoretical Reasoning and Empirical Testing

摘要

The effect of prior probabilities in the maximum likelihood classification on individual classes receives little attention, and this is addressed in this paper. Prior probabilities are designed only for overlapping spectral signatures. Accordingly, their effect on an individual class is independent of the classes that are spectrally separable from this class. The theoretical reasoning reveals that an increased prior probability, which shifts the decision boundary away from the class mean, will increase the assignment and boost the producer's accuracy as compared to the use of equal priors; though the change of the user's accuracy is not constant, it is expected to decrease in most cases. The tendency is just the opposite when a lower prior probability is used. A case study was conducted using Landsat tm data provided along with erdas Imagine~R software. Two other pieces of evidence derived from the published literature are also presented.