  Observations of seabed texture with synthetic aperture sonar are dependent
upon several factors. In this work, we focus on collection geometry with
respect to isotropic and anisotropic textures. The low grazing angle of the
collection geometry, combined with orientation of the sonar path relative to
anisotropic texture, poses a significant challenge for image-alignment and
other multi-view scene understanding frameworks. We previously proposed using
features captured from estimated seabed relief to improve scene understanding.
While several methods have been developed to estimate seabed relief via
intensity, no large-scale study exists in the literature. Furthermore, a
dataset of coregistered seabed relief maps and sonar imagery is nonexistent to
learn this domain translation. We address these problems by producing a large
simulated dataset containing coregistered pairs of seabed relief and intensity
maps from two unique sonar data simulation techniques. We apply three types of
models, with varying complexity, to translate intensity imagery to seabed
relief: a Gaussian Markov Random Field approach (GMRF), a conditional
Generative Adversarial Network (cGAN), and UNet architectures. Methods are
compared in reference to the coregistered simulated datasets using L1 error.
Additionally, predictions on simulated and real SAS imagery are shown. Finally,
models are compared on two datasets of hand-aligned SAS imagery and evaluated
in terms of L1 error across multiple aspects in comparison to using intensity.
Our comprehensive experiments show that the proposed UNet architectures
outperform the GMRF and pix2pix cGAN models on seabed relief estimation for
simulated and real SAS imagery.
