Signal and Image Processing with Side Looking Sonar

Mines placed on the sea floor are still a vast threat in civil and military shipping. This potential risk is typically encountered by advanced sonar signal processing techniques and time consuming manual evaluation of the sonar data by a human operator. Due to mission specific time constraints a computer aided or even autonomous analysis of the huge amount of data is desired. Acoustic waves suffer much less in seawater and therefore sonar sensors are the prevalent to be used in under water. A main difficulty is to extract information from side looking sonar (SLS). An overview of SLS approaches to underwater imaging is given in this paper. A short outlook to the system operation is also presented. This paper proposes a data model to generate SLS images that is suitable for identifying regions of interest (ROI) in sonar images, in particular the detection of anomalies on the sea floor with focus on proud ground mines. It also presents a method to classify maps of the seafloor from sonar imagery. The techniques described are directly applicable to a range of remote sensing problems.