A Dempster-Shafer Relaxation Approach to Context Classification

Abstract

A relaxation scheme is proposed in which Dempster-Shafer evidential theory is used to bring the effect of the spatial neighborhood of a pixel into a classification. The benefits include the ability to incorporate uncertainty in the neighborhood information, allowing a stopping criterion to be devised based on increasing the uncertainty contribution of the neighborhood to unity within a prescribed number of iterations. The number of iterations to be used is governed by several factors, including an estimate of how far out in the neighborhood pixels are assumed to be influential. As with standard relaxation labeling, but unlike many other context-sensitive methods, the evidential approach can be initialized from the results of a separate point statistical classification of the image; it is also consistent with multisource analyses based on evidential methods for fusion. A variation of evidential relaxation using considerably simplified neighborhood information is also developed, illustrating that very good results can be obtained without detailed knowledge of the spatial properties of a scene. The new procedures are compared experimentally with standard probabilistic relaxation and the application of Markov random fields.