Inferring depth contours from sidescan sonar using convolutional neural nets

Sidescan sonar images are 2D representations of the seabed. The pixel location encodes distance from the sonar and along track coordinate. Thus one dimension is lacking for generating bathymetric maps from sidescan. The intensities of the return signals do, however, contain some information about this missing dimension. Just as shading gives clues to depth in camera images, these intensities can be used to estimate bathymetric profiles. The authors investigate the feasibility of using data driven methods to do this estimation. They include quantitative evaluations of two pixel‐to‐pixel convolutional neural networks trained as standard regression networks and using conditional generative adversarial network loss functions. Some interesting conclusions are presented as to when to use each training method.