Classification and Analysis of Seafloor Sediments Using Hidden Features From Combined Imaging Mechanism and Seafloor Reflection Model

To enable side-scan sonar (SSS) to be used for large-scale seabed sediment detection, classification, and composition analysis, this article breaks through the limitations of past superficial use of echo characteristics and proposes a hidden feature extraction method that combines imaging mechanisms with seabed reflection models. By using the penetration characteristics of sound waves, weak echo width (WEW) features were extracted from the water column region of SSS echoes. By using the Lambertian model and the concept of denoising diffusion probabilistic models (DDPMs), seabed topography and sediment reflection features were, furthermore, successfully decomposed from SSS echoes, and a method to extract features highly correlated with sediment composition from these components is also presented. The effectiveness of these hidden features was demonstrated through sediment classification and analysis experiments in the Bohai Bay of China. Compared to using only echo features, the hidden features extracted in this article improved classification accuracy by over 20% on common classifiers. Based on the hidden features and sediment distribution, the causes of seabed composition near Caofeidian were explained. Additionally, through feature importance and sediment response experiments, the response mechanisms of different hidden features to various sediment types were scientifically interpreted. This study demonstrates the significant application value of SSS in large-scale seabed sediment detection, classification, and analysis for geoscientific tasks.