Optimal Scale Determination for Object-Based Backscatter Image Analysis in Seafloor Substrate Classification Based on Classification Uncertainty

Backscatter image can be used to extract features for seafloor substrate classification. Currently, object-based method is a mainstream solution for image analysis. However, determining the optimal scale for image segment using traditional object-based method is a challenge. In this letter, we proposed an automatic method to select the optimal scale for each backscatter image pixel based on classification uncertainty. Then, the backscatter image features are extracted at the optimal scale and integrated with the bathymetric features to form the normalized multibeam echo sounder (MBES) features. Finally, a supervised classification model is established by relating the MBES features and ground truth data. Experimental results indicate that the overall accuracy reaches 85.41% and the kappa coefficient is 0.7935, which proved that the proposed method performed better than the traditional methods and can be transferable to other studies.