CUG-STCN: A seabed topography classification framework based on knowledge graph-guided vision mamba network
CUG-STCN: A seabed topography classification framework based on knowledge graph-guided vision mamba network
Blog Article
Multibeam sounding is a high-precision remote sensing method for seabed detection.Seabed topography classification is crucial for marine science research, resource exploration and engineering.When using multibeam data for seabed topography automatic classification, the fuzzy ANTI-COLD- CLINICAL STRENGTH boundaries of different topographic entities, redundancy of multimodal data, and the lack of geological knowledge guidance have led to low classification accuracy.
Thus, a knowledge graph-guided vision mamba seabed topography classification network (CUG-STCN) was constructed, consisting of three modules: (1) The long sequence modeling mamba-based encoder addresses the fuzzy seabed topography boundary.It uses 2D-selective-scan to create image blocks in different scanning directions.By combining with the selective state space model to capture long-range dependencies and ensure transmission of spatial context information while maintaining linear computational complexity.
(2) The cross-modal information interaction and fusion module addresses the redundancy of multimodal information.By employing a Clothing - Womens Bottoms - Shorts bidirectional information interaction mechanism, it captures the correlations of seabed topography between different modalities and achieving feature fusion.(3) The seabed topography knowledge graph-guided semantic perception module guides the geological knowledge.
It constructs seabed topography knowledge vectors through entity query and word embedding, using the similarity between vectors to create a similarity measurement matrix.It provides geological knowledge, enhancing the modeling capability of complex seabed topography relationship.CUG-STCN achieves OA of 90.
11% and mIOU of 48.50%, outperforming six mainstream networks, which at most, achieve the OA and mIOU improvements of 5.37% and 14.
18%.Notably, the application of CUG-STCN in other regions demonstrates its strong generalization performance.