Unified Design of a Feature-Based ADAC System for Mine Hunting Using Synthetic Aperture Sonar

A system for automatic detection and classification (ADAC) of underwater objects for mine hunting applications is proposed. The system consists of three steps: segmentation, feature extraction, and classification. This paper focuses on two design issues: the selection of the optimal classifier and the selection of the optimal feature subset. Often, the comparison of classification systems is based on a pre-selected feature set. However, a different subset might yield a different ranking. We apply a resampling algorithm that assesses the classifier performance without constraints to any specific feature subset. Once a classifier is chosen, a feature selection algorithm estimates the optimal feature subset. We propose a novel extension of the sequential forward selection (SFS) and the sequential forward floating selection (SFFS) methods, which mitigates their main limitations, i.e., the nesting problem. Instead of keeping the best alternative at each iteration, a set of D options is stored. The performance of the so-called D-SFS and D-SFFS is tested on simulated and real data, significantly outperforming the standard algorithms. The proposed methods are also used for designing an ADAC system for mine hunting based on two extensive databases of synthetic aperture sonar images.