Classification of 3D Sonar Point Clouds derived Underwater using Machine and Deep Learning (CANUPO and RandLA-Net) Approaches

Abstract

The techniques of point cloud classification in aquatic environments have various applications such as landslide hazard mapping, recovery of lost objects, underwater infrastructure inspection, exploration of mineral resources on the seabed, underwater cultural heritage documentation, environmental preservation and conservation purposes. This study combines acoustic (Sonar) and laser-based (Lidar) remote sensing technologies in an aquatic environment with two machine and deep learning approaches to illustrate the techniques to identify submerged objects. Firstly, the relative accuracy of the underwater imaging system, the BlueView BV5000 Mechanical Scanning Sonar, is evaluated at close range. Secondly, the supervised CANUPO and RandLA-Net classification approaches are used to classify submerged sonar point clouds. Common objects of interest, namely tyres and chairs, were selected for classification. Relative accuracy measurement results showed a centimetre-level root mean square error (RMSE) value, with good accuracies recorded when the scanner is positioned close to objects. The best results were achieved when the target objects were placed at a minimum distance of 2 m from the acoustic scanner. Subsequently, the results of point cloud classification were satisfactory for both approaches. An overall accuracy of 79.81% and an   F₁ score of 79.80% were achieved using the CANUPO classification approach. On the other hand, an 80.72% overall accuracy and an 80.63% F₁ score were obtained using a RandLA-Net approach. These analyses provide a reasonable framework for the parameters that can be used when applying these techniques in natural aquatic environments.