Unsupervised Terrain Reconstruction From Side-Scan Sonar Constrained to the Imaging Mechanism

To meet the demands of marine scientific research and ocean engineering construction for cost-effective, high-resolution, and large-scale acquisition of seafloor topography, this study proposes an unsupervised terrain reconstruction technique for side-scan sonar (SSS). Unlike conventional methods, this technique does not rely on externally measured depths for initial terrain and ground truth; instead, it constructs these elements based on its own imaging mechanism. Through the development of a rigorous seafloor reflection model, we have designed an unsupervised depth convolutional neural network (DCNN) inversion framework. This framework utilizes the depth of the sea bottom line as the initial terrain and employs the height calculated from the target’s shadow as the ground truth. The proposed network not only extracts absolute topographical information but also captures relative changes in beam pattern and seabed substrate properties. The experimental results demonstrate the method’s excellent accuracy in terrain inversion for areas with water depths ranging from 10 to 14 m, achieving a vertical accuracy exceeding 0.17 m. Moreover, the method exhibits robust resilience to residual radiometric distortions, variations in seabed substrate, noise, and shadows. This innovative approach provides a reliable methodology for acquiring and broadly applying high-resolution seafloor terrain, potentially advancing various fields of marine research and engineering.