Juan Wang’s research while affiliated with State Oceanic Administration and other places

The seabed sediment classification plays an im-portant part in marine ecological environment protection and other related fields. To fully explore the application ability of marine geographic information in seabed sediment classification, the paper makes a contribution to overcome the low accuracy and reliability shortcomings of using single data source and traditional classifiers. Based on extracted multi-source features, the scale invariant feature transform (SIFT) - random sample consensus (RANSAC) model is applied to realize feature-level fusion be-tween airborne LiDAR bathymetry (ALB) data and multispectral remote sensing images. Furthermore, a dual branch convolutional neural networks (CNN) classifier is constructed to classify the seabed sediment into five categories (coral reef, sand, gravel, coastal zone, and vegetation). To verify the effectiveness of fused data in seabed sediment classification, experiments were con-ducted using multispectral remote sensing images and ALB data. Experimental results show that the overall classification accuracy and Kappa coefficient of the dual CNN classifier constructed in this paper are 98.2% and 0.977, respectively. In addition, the classification results using multi-source fusion data are higher than those using single-source data, indicating the accuracy and effectiveness of multi-source fusion features for classification. The research results can provide effective technical support for seabed sediment classification.