An Information Theoretic Approach for In-Situ Underwater Target Classification

This paper introduces a method for in-situ underwater target classification, based on an image retrieval system, that can be implemented using a simple two-layer kernel-based network. This system incorporates a learning mechanism that captures new information for discriminating between objects in different classes or within the same class from a set of input-output pairs with associated confidence scores. A strategy to select the most informative patterns for optimal parameter adaptation during in-situ learning is also described. The system is then tested on a database of synthetically generated sonar images. The ability of the system to correctly classify images containing objects in different environmental and operating conditions than those used for original training, as well as its ability to incorporate new object types without perturbing the classification performance on other object types are demonstrated.