Adaptive Fusion Architecture for Context Aware Detection and Classification

This paper presents a framework for the fusion of detection and classification algorithms for side-scan imagery. The framework is based on Dempster-Shafer theory of evidence, which permits the fusion of heterogeneous outputs of targets detectors and classifiers. The paper will illustrate how the technique permits the incorporation of contextual information into the decision process, giving more importance to the outputs of those algorithms that perform better in particular mission conditions.