Open-source synthetic aperture sonar simulation datasets

There are many interesting machine learning (ML) applications in underwater acoustics. However, some ML algorithms require large amounts of training and testing data and there is a lack of open-source data for these purposes. The Point-based Sonar Signal Model (PoSSM) is a useful tool that generates synthetic time-series data appropriate for coherent signal processing applications. One underwater acoustics application is the identification of objects in synthetic aperture sonar (SAS) imagery. This paper introduces multiple datasets that provide a collection of SAS imagery from a generic SAS sonar above multiple seafloor textures and bathymetries. A variety of objects (e.g., cylinders, rocks, lobster traps) are contained in the imagery. This collection of synthetic data is suitable for training and testing ML algorithms that span a range of complexity, from constant false alarm rate (CFAR) automated detectors to convolutional neural networks (CNNs). These algorithms can perform a variety of tasks that include object detection and classification. In this paper, we test a CFAR detector on image data to estimate object locations. An example use-case of the synthetic data is shown via the training and evaluation of CNN-based classifiers for object recognition.