A principled physics-based approach for data augmentation with synthetic aperture sonar (SAS) imagery is proposed. The approach is based on partitioning the wavenumber spectrum of the data. The images that result from retaining only specific sectors of spectral content are referred to as “ghosts.” The approach enables the generation of practically infinite mildly correlated images: high enough that key fundamental features of objects persist, but low enough to engender desired data diversity. The ghosts can be used to help train data-hungry convolutional neural networks (CNNs), but they can also be leveraged at inference time to provide a more robust ensemble prediction that also carries with it a measure of uncertainty. Experimental results on an object classification task with real, measured SAS data highlight the benefits of the approach.

Automatic target recognition (ATR) for 3D synthetic aperture sonar (SAS) imagery is an intrinsic challenge in highly cluttered ocean environments, especially for objects partially or completely buried in the sediment. Conventional dynamic range compression (DRC) techniques such as log-compression, which is a type of tone mapping intended to appeal to the human visual system, can further obscure the sonar signatures of these already physically occluded objects and lead to suboptimal downstream ATR performance, particularly for convolutional neural networks (CNNs). In this paper, we present a novel machine learning-based approach for tone mapping sub-bottom SAS imagery as a pre-processing stage in the 3D SAS ATR pipeline. This learned tone mapping function can be jointly optimized with a CNN-based ATR algorithm. We train and validate our method on measured volumetric SAS data captured by the Sediment Volume Search Sonar (SVSS) system.

Underwater sites affected by unexploded ordnance (UXO) pose significant risk to both human safety and environmental well-being. Sonar imaging is commonly employed to investigate such sites and aid in UXO remediation efforts. However, manually identifying and classifying potential targets in sonar data is challenging and time-consuming. Previous research has explored the use of machine learning models to recognize and categorize targets; however, many of these approaches lack transparency and fail to consider the underlying physical acoustics. Additionally, acquiring sufficient training data for these models can often be problematic. In this study, we present a novel approach by designing neural networks that explicitly account for the unique physics involved in the problem domain. UXOs examined using low-frequency sound frequently exhibit resonant behavior, where the sound is re-radiated after initial geometric scattering, owing to the elastic and compressional properties of the objects. Moreover, such resonant effects are typically absent in clutter objects, making them advantageous in discriminating UXOs from non-UXOs. Consequently, we propose several neural network architectures that leverage these resonant effects, utilizing 3D data obtained from a synthetic aperture sonar (SAS) imaging sonar. Our first proposal incorporates a recurrent neural network to model the physics-based correlation among adjacent time/spatial slices, originating from the resonant phenomena. For our second proposal, we employ intensity imagery of orthogonal projections of the 3D data cube, which capture shape-specific resonant scattering mechanisms unique to specific types of UXOs. To evaluate the effectiveness of our methods, we compare them against recent state-of-the-art algorithms using a real-world 3D SAS dataset. Remarkably, even when confronted with limited training data, our approaches consistently demonstrate superior results. Our findings highlight the significant potential of incorporating physical acoustics into neural network designs for UXO detection and classification, offering improved accuracy and efficiency in underwater remediation operations.

Classification of objects based on acoustic remote sensing is influenced by a variety of scattering phenomena, both in-air and underwater. Active sonar ensonification induces multiple types of acoustic scattering phenomena including direct geometric scattering as well as structural resonance. It is observed that the choice of representation for the raw measurement affects the shape and strength of discriminatory features and the performance of classifiers that utilize them. Using in-air acoustic measurements collected in a noise-controlled laboratory setting, this work develops a statistical model for discriminatory features and a framework to identify the discriminatory pixels in multiple representations as well as an approach to quantify their discriminatory capacity in the presence of additive noise of varying levels. This framework is used to assess the utility and robustness of multiple representation for object classification over a wide range of noise levels, and to compare the relative classification performance bounds under independent pixels assumption as well as conventional feature energy detectors.

Synthetic aperture sonar (SAS) measures a scene from multiple views in order to increase the resolution of reconstructed imagery. Image reconstruction methods for SAS coherently combine measurements to focus acoustic energy onto the scene. However, image formation is typically under-constrained due to a limited number of measurements and bandlimited hardware, which limits the capabilities of existing reconstruction methods. To help meet these challenges, we design an analysis-by-synthesis optimization that leverages recent advances in neural rendering to perform coherent SAS imaging. Our optimization enables us to incorporate physics-based constraints and scene priors into the image formation process. We validate our method on simulation and experimental results captured in both air and water. We demonstrate both quantitatively and qualitatively that our method typically produces superior reconstructions than existing approaches. We share code and data for reproducibility.

Underwater sites impacted by unexploded ordnance (UXO) may pose an unacceptable risk to human and environmen-tal health. Sonar imaging is commonly used to interrogate such sites during UXO remediation, however manually identifying and classifying potential targets is difficult and time intensive. Previous work has explored training machine learning models to recognize and classify targets, however many of these “black-box” approaches fail to model the underlying physical acoustics and require abundant training data which is often hard to obtain. Specifically, UXOs interrogated with low frequency sound often exhibit resonant behavior which re-radiates the sound after the initial scattering due to elastic and compressional properties of the object. Such effects are usually not present in clutter objects, making them advantageous in discriminating UXO from non-UXO. In this work, we propose two neural networks which specifically model resonant scattering effects in order to find UXO from a 3D synthetic aperture sonar (SAS) imaging sonar. We do this by utilizing sequence models which are efficient at modeling the spatially correlated nature of the resonant scattering features. We compare our proposal to two recent state-of-the-art algorithms on a real-world 3D SAS dataset and show superior results even when limited training data is available.

This paper presents a method for navigating relative to the sea floor using sonar imagery. The aspect dependent terrain recognition problem is addressed by generating acoustic holograms of the sea floor containing images of all aspects of interest. Grazing angle compensation is applied to reduce the required spatial sampling from planar to linear. Sonar images are coherently correlated with centimeter resolution to provide navigational updates. The technique is demonstrated using five short duration missions in St. Andrew Bay and three long duration missions, with a combined extent of 69.7 km, in the Gulf of Mexico.