Underwater navigation and localization are greatly enhanced by the use of acoustic images. However, such images are of difficult interpretation. Contrarily, aerial images are easier to interpret, but require Global Positioning System (GPS) sensors. Due to absorption phenomena, GPS sensors are unavailable in underwater environments. Thus, we propose a method to translate sonar images acquired underwater to an aerial counterpart. This process is called sonar-to-satellite translation. To perform the conversion, a U-Net based neural network is proposed, enhanced with state-of-the-art techniques, such as dilated convolutions and guided filters. Afterwards, our approach is validated on two datasets containing sonar images and their satellite analogue. Qualitative experimental results indicate that the proposed method can transfer features from acoustic images to aerial images, generating satellite images that are easier to interpret and visualize. [ABSTRACT FROM AUTHOR]