Leveragable Adaptive Multi-Scale Features and Learnable Prototypes for Imbalanced Sea State Estimation Based on Ship Motion Data

The adoption of autonomous vessels has been accelerated by the flourishing maritime economy and more stringent shipping regulations. Accurate sea state estimation (SSE) is of paramount importance for their safe operation. Traditional SSE methods face several limitations, including subjectivity in manual observation, high radar costs, and the insufficient timeliness of satellite data. In contrast, SSE methods based on ship motion data, particularly deep learning models, offer advantages in capturing complex nonlinear relationships. However, challenges persist, such as imbalanced sea state data, suboptimal performance under extreme conditions, and the complexity of ship motion data, which hampers effective feature extraction. To address these challenges, this paper proposes a novel deep learning model that integrates a dynamic attention mechanism with adaptive selective kernel modules and multi-scale feature fusion to capture spatiotemporal dependencies. Additionally, an enhanced prototype classifier, utilizing cosine similarity and dynamic prototype updating, is introduced to mitigate data imbalance and improve model robustness in extreme conditions. To evaluate the performance of the proposed model, we conducted experiments on 30 benchmark datasets for multivariate time series classification, as well as two sea state datasets. The comparison results demonstrate that our model outperforms most baseline methods in general multivariate time series classification tasks and also surpasses state-of-the-art methods for SSE and class imbalance learning. Furthermore, the experimental results show that the proposed model is robust to data noise and missing values.