Estimation of the probability of error without ground truth and known a priori probabilities

The probability of error or, alternatively, the probability of correct classification (PCC) is an important criterion in analyzing the performance of a classifier. Labeled samples (those with ground truth) are usually employed to evaluate the performance of a classifier. Occasionally, the numbers of labeled samples are inadequate, or no labeled samples are available to evaluate a classifier's performance; for example, when crop signatures from one area from which ground truth is available are used to classify another area from which no ground truth is available. This paper reports the results of an experiment to estimate the probability of error using unlabeled test samples (i.e., without the aid of ground truth).