Joint feature selection and improved ensemble learning model for seabed sediments classification

Aiming at the classification of seabed sediments data with complex type acquired by side scan sonar, a data self-driven method based on joint feature selection and improved Stacking model is proposed to accomplish the classification of seabed sediments. The method firstly uses the ReliefF algorithm to extract effective low-dimensional features based on the multi-domain features of the seabed scattering data, and then combines the artificial fish swarm algorithm with Stacking model to form improved ensemble learning classifier to complete the classification of seabed sediments. The results of marine data processing show that the method can classify various types of seabed sediments, and the classification accuracy, Kappa coefficient and F1-score reach 88.55%, 0.857 and 0.887, respectively, indicating the effectiveness of this method.