Methods for Predicting Multichannel Images Classification Efficiency

A method for predicting probability characteristics for algorithms of supervised classification of multichannel images is proposed. Implementation of quasi-Bayesian image recognition strategy in conditions of incomplete a priori information about a satellite image is considered. Total weighted probability of correct class recognition is taken as a criterion of effectiveness of decision rule. Weights are interpreted as estimates of a priori probability for occurrence of precedents of the corresponding classes. The procedure for estimating a priori probabilities of classes by Monte Carlo method based on the results of fuzzy classification of samples with fixed size is described; the empirical dependence of the relative error of estimates on the sample size is given.