Classification of morphologically separated synthetic aperture sonar imagery via dual-representation convolutional neural networks

Morphological component analysis (MCA) involves the separation of time series into components based on morphological factors, such as duration or envelope type. Acoustic returns from sonar systems can be separated into short-duration and long-duration components using MCA, and the resulting time series formed into traditional synthetic aperture sonar (SAS) images. We present an application of this approach to classification of data from hollow and solid spherical targets collected using a linear in-air SAS system (AirSAS). AirSAS time series were separated into morphological components and, utilizing online image formation, the resulting imagery processed through a multi-branch convolutional neural network (CNN) architecture to classify targets by type. Classification results are included for a variety of scene backgrounds and representation modalities. This is a challenging classification problem since the targets have the same exterior geometry and are distinguished by non-specular acoustic phenomenology.