Experts Fusion and Multilayer Perceptron Based on Belief Learning for Sonar Image Classification

TL;DR

This paper introduces a method combining expert opinions and belief learning with multilayer perceptrons to improve sonar image classification in uncertain seabed environments.

Contribution

It proposes a novel approach that manages conflicting expert interpretations using belief learning for more robust seabed classification.

Findings

Effective handling of conflicting expert opinions.

Improved classification accuracy on real sonar images.

Robust seabed characterization in uncertain environments.

Abstract

The sonar images provide a rapid view of the seabed in order to characterize it. However, in such as uncertain environment, real seabed is unknown and the only information we can obtain, is the interpretation of different human experts, sometimes in conflict. In this paper, we propose to manage this conflict in order to provide a robust reality for the learning step of classification algorithms. The classification is conducted by a multilayer perceptron, taking into account the uncertainty of the reality in the learning stage. The results of this seabed characterization are presented on real sonar images.