Probabilistic Cluster Labeling of Imagery Data

In this paper the author considers the problem of obtaining the probabilities of class labels for the clusters using spectral and spatial information from a given set of labeled patterns and their neighbors. A relationship is developed between class and cluster conditional densities in terms of probabilities of class labels for the clusters. Expressions are presented for updating the a posteriori probabilities of the classes of a pixel using information from its local neighborhood. Fixed-point iteration schemes are developed for obtaining the optimal probabilities of class labels for the clusters. These schemes utilize spatial information and also the probabilities of label imperfections. Furthermore, experimental results from the processing of remotely sensed multispectral scanner imagery data are presented.