Semisupervised Local Fusion Approach for Mine Detection in SONAR Data

Abstract

In this paper, we propose a novel approach for automatic mine detection in SOund NAvigation and Ranging (SONAR) data. The proposed framework relies on possibilistic-based fusion method to classify SONAR instances as mine or mine-like object. The proposed semisupervised algorithm minimizes some objective function, which combines context identification, multi-algorithm fusion criteria, and a semisupervised learning term. The optimization aims to learn contexts as compact clusters in subspaces of the high-dimensional feature space via possibilistic semisupervised learning and feature discrimination. The semisupervised clustering component assigns degree of typicality to each data sample to identify and reduce the influence of noise points and outliers. Then, the approach yields optimal fusion parameters for each context. The experiments on synthetic data sets and standard SONAR data set show that our semisupervised local fusion outperforms individual classifiers and unsupervised local fusion.