Long-tailed ship recognition method based on aerial-space multimodal perception

In the context of integrated aerial-space ocean monitoring and intelligent maritime management, ship target recognition based on multisource sensing data collected from Unmanned Aerial Vehicles (UAVs) and satellites plays a crucial role in navigation control, maritime law enforcement, and border surveillance. However, real-world ship recognition tasks face two major challenges. First, multimodal data fusion is difficult due to the heterogeneity and spatiotemporal misalignment between different modalities, such as optical images and electromagnetic radiation signals. Second, ship categories naturally exhibit a severe long-tailed distribution, where head classes dominate the sample population while tail classes remain scarce, significantly degrading overall recognition performance. To address these challenges, this paper proposes a long-tailed ship recognition method oriented toward aerial-space multimodal perception. The proposed method integrates a class-aware boundary optimization strategy and a category-based reweighting mechanism, effectively enhancing the discriminative capability of tail classes and improving the robustness of multimodal fusion. Experimental results demonstrate that the proposed method consistently outperforms existing approaches on representative long-tailed ship recognition tasks, showing strong practicality and generalization capability.