Fuzzy Rules and Evidence Theory for Satellite Image Analysis

TL;DR

This paper introduces a fuzzy rule-based classifier enhanced with Dempster-Shafer evidence theory for improved multispectral satellite image classification, demonstrating superior performance over existing methods.

Contribution

It presents a novel combination of fuzzy rules and Dempster-Shafer theory to enhance satellite image classification accuracy.

Findings

Improved classification accuracy with D-S theory integration.

Better performance than existing methods on tested images.

Consistent performance enhancement across all test cases.

Abstract

Design of a fuzzy rule based classifier is proposed. The performance of the classifier for multispectral satellite image classification is improved using Dempster- Shafer theory of evidence that exploits information of the neighboring pixels. The classifiers are tested rigorously with two known images and their performance are found to be better than the results available in the literature. We also demonstrate the improvement of performance while using D-S theory along with fuzzy rule based classifiers over the basic fuzzy rule based classifiers for all the test cases.