Evidential Reasoning in Image Understanding

TL;DR

This paper explores the use of evidential reasoning, specifically Dempster-Shafer theory, for classifying multispectral remote sensing images, comparing it with Bayesian and other methods.

Contribution

It introduces the application of Dempster-Shafer evidential reasoning to multispectral image classification and compares its performance with existing Bayesian and dynamic programming approaches.

Findings

Dempster-Shafer approach yields competitive classification results.

Evidential reasoning provides a different perspective on contextual classification.

Comparison shows advantages and limitations of each method.

Abstract

In this paper, we present some results of evidential reasoning in understanding multispectral images of remote sensing systems. The Dempster-Shafer approach of combination of evidences is pursued to yield contextual classification results, which are compared with previous results of the Bayesian context free classification, contextual classifications of dynamic programming and stochastic relaxation approaches.