Synthetic Aperture Sonar

Synthetic aperture sonar (SAS) uses multiple sonar measurements to create high-resolution images of underwater environments, overcoming limitations of traditional sonar. Current research focuses on improving SAS image processing and analysis using deep learning techniques, including self-supervised and weakly-supervised learning methods, and novel architectures like UNets and conditional GANs to address challenges like limited labeled data and complex seabed textures. These advancements are crucial for enhancing underwater object detection, classification, and scene understanding, with applications ranging from seabed mapping to target recognition in both civilian and military contexts.