  Underwater automatic target recognition (UATR) has been a challenging
research topic in ocean engineering. Although deep learning brings
opportunities for target recognition on land and in the air, underwater target
recognition techniques based on deep learning have lagged due to sensor
performance and the size of trainable data. This letter proposed a framework
for learning the visual representation of underwater acoustic imageries, which
takes a transformer-based style transfer model as the main body. It could
replace the low-level texture features of optical images with the visual
features of underwater acoustic imageries while preserving their raw high-level
semantic content. The proposed framework could fully use the rich optical image
dataset to generate a pseudo-acoustic image dataset and use it as the initial
sample to train the underwater acoustic target recognition model. The
experiments select the dual-frequency identification sonar (DIDSON) as the
underwater acoustic data source and also take fish, the most common marine
creature, as the research subject. Experimental results show that the proposed
method could generate high-quality and high-fidelity pseudo-acoustic samples,
achieve the purpose of acoustic data enhancement and provide support for the
underwater acoustic-optical images domain transfer research.
