Enhanced automated recognition of underwater mine-like objects through environmentally adaptive fusion of detectors, features, and classifiers

Abstract

The complexity of the natural underwater environment creates a challenging arena in which to find underwater mines. In this work, we demonstrate that automated mine-like object detection tasks are greatly facilitated by a comprehensive fusion process. Our approach begins with characterization of the seafloor based on textures within synthetic aperture sonar (SAS) imagery and uses this to exploit information from the available sensors, multiple detector types, measured features, and target classifiers, to facilitate mine-like object recognition. Our approach is able to adapt as environmental characteristics change, including the ability to recognize novel seabed types. We then adaptively retrain classifiers through active learning in these novel seabed types resulting in improved mitigation of challenging environmental clutter as it is encountered, and develop a segmentation constrained network (SCN) algorithm which enables increased generalization abilities for recognizing mine-like objects in both under-represented and novel, unseen environments in available training data. Additionally, we present a fusion approach that allows us to combine multiple detectors, feature types spanning both measured expert features and deep learning, and an ensemble of classifiers, for the particular seabed mixture proportions measured around each detected target. [Work supported by the Office of Naval Research.]