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

Source: [Experts Fusion and Multilayer Perceptron Based on Belief Learning for Sonar Image Classification](<https://www.sota2.com/research/paper/68fa5ff11e209131ce9ecd19>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:24.110Z

Authors: Arnaud Martin, Christophe Osswald

Published: 2008-06-12T06:44:55.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/0806.2007>)

[PDF](<https://arxiv.org/pdf/0806.2007.pdf>)