Few-Shot Classification of Shallow-Water Seabed Sediment and Benthic Cover by Fusing Airborne LiDAR Bathymetry and Multispectral Imagery

The accurate classification of seabed sediment and benthic covers in shallow-water environments remains a key challenge for marine activities and oceanographic research. However, coastal areas of shallow waters are influenced by complex dynamic environments, making it difficult to obtain authentic sediment and benthic-cover samples. Therefore, to address the problem of few-shot classification of seabed sediment and benthic covers, a few-shot classification algorithm of seabed sediment and benthic covers based on the fusion model of airborne LiDAR bathymetry (ALB) and multispectral images is proposed in this article. Based on the extracted features, a scale-invariant feature transform-progressive sample consensus (SIFT-PROSAC) algorithm and perspective transform model were constructed to achieve feature fusion. Then, multi-modal feature selection is realized using a formal concept analysis-Relief-F (FCA-Relief-F) algorithm. Finally, a graph attention network-prototype network (GAT-PN) model was established to classify five types of sediment and benthic cover (coral reef, stone, sand, vegetation, and coastal zone). To validate the effectiveness of the proposed method, experimental data from actual measurements at Ganquan Island in the Xisha Islands of China were used. Compared to other classical classifiers, the GAT-PN algorithm achieves a higher classification accuracy, with an overall accuracy (OA) and Kappa coefficient of 97.50% and 0.97, respectively. The findings of this study provide effective technical support for marine engineering and related fields.