Sonar Image Composition for Semantic Segmentation Using Machine Learning

Abstract

This paper presents an approach for merging side scan sonar data and bathymetry information for the benefit of improved automatic shipwreck identification. The steps to combine a raw side-scan sonar image with a 2D relief map into a new composite RGB image are presented in detail, and a supervised image segmentation approach via the U-Net architecture is implemented to identify shipwrecks. To validate the effectiveness of the approach, two datasets were created from shipwreck surveys: one using side-scan only, and one using the new composite RGB images. The U-Net model was trained and tested on each dataset, and the results were compared. The test results show a mean accuracy which is 15% higher for the case where the RGB composition is used when compared with the model trained and tested with the side-scan sonar only dataset. Furthermore, the mean intersection over union (IoU) shows an increase of 9.5% using the RGB composition model.