On the Utility of Multiple Sonar Imaging Bands for Underwater Object Recognition

Abstract

A new, two-stage approach for performing automatic target recognition (ATR) in multi-band synthetic aperture sonar (SAS) imagery is proposed. The approach consists of a simple detection algorithm, called MondrianB, followed by a more sophisticated classification stage based on convolutional neural networks (CNNs). The approach is extensible in that the same method can be used, without any modifications, for sensor data in the form of an arbitrary number of co-registered image bands (including one). The detection stage does not require any labeled training data. The classification stage relies on ensembles of small, efficient CNNs that collectively provide robust alarm predictions by leveraging unique network architectures and multiple input-data representations. The performance of the proposed ATR approach is verified on a sizable set of data collected at sea by a three-band SAS system. Specifically, the experiments conducted quantity the utility of each imaging band on both the detection and classification stages of a man-made object-recognition task. It is shown that leveraging information from the additional bands improves both detection and classification performance.