Seabed sediment classification is of great significance in acoustic remote sensing. To accurately classify seabed sediments, big data are needed to train the classifier. However, acquiring seabed sediment information is expensive and time-consuming, which makes it crucial to design a well-performing classifier using small-sample seabed sediment data. To avoid data shortage, a self-attention generative adversarial network (SAGAN) was trained for data augmentation in this study. SAGAN consists of a generator, which generates data similar to the real image, and a discriminator, which distinguishes whether the image is real or generated. Furthermore, a new classifier for seabed sediment based on self-attention densely connected convolutional network (SADenseNet) is proposed to improve the classification accuracy of seabed sediment. The SADenseNet was trained using augmented images to improve the classification performance. The self-attention mechanism can scan the global image to obtain global features of the sediment image and is able to highlight key regions, improving the efficiency and accuracy of visual information processing. The proposed SADenseNet trained with the augmented dataset had the best performance, with classification accuracies of 92.31%, 95.72%, 97.85%, and 95.28% for rock, sand, mud, and overall, respectively, with a kappa coefficient of 0.934. The twelve classifiers trained with the augmented dataset improved the classification accuracy by 2.25%, 5.12%, 0.97%, and 2.64% for rock, sand, mud, and overall, respectively, and the kappa coefficient by 0.041 compared to the original dataset. In this study, SAGAN can enrich the features of the data, which makes the trained classification networks have better generalization. Compared with the state-of-the-art classifiers, the proposed SADenseNet has better classification performance.

This paper explores the application of Deep Reinforcement Learning (DRL) to optimize adaptive observation strategies for multi-AUV systems in complex marine environments. Traditional algorithms struggle with the strong coupling between environmental information and observation modeling, making it challenging to derive optimal strategies. To address this, we designed a DRL framework based on the Dueling Double Deep Q-Network (D3QN), enabling AUVs to interact directly with the environment for more efficient 3D dynamic ocean observation. However, traditional D3QN faces slow convergence and weak action–decision correlation in partially observable, dynamic marine settings. To overcome these challenges, we integrate a Gated Recurrent Unit (GRU) into the D3QN, improving state-space prediction and accelerating reward convergence. This enhancement allows AUVs to optimize observations, leverage ocean currents, and navigate obstacles while minimizing energy consumption. Experimental results demonstrate that the proposed approach excels in safety, energy efficiency, and observation effectiveness. Additionally, experiments with three, five, and seven AUVs reveal that while increasing platform numbers enhances predictive accuracy, the benefits diminish with additional units.

Accurately predicting Arctic sea ice concentration (SIC), especially during the melting season at sub-seasonal scales, is essential for advancing our knowledge of global climate change. Currently, statistical models for SIC prediction face three major challenges: limited spatial resolution and forecast timeliness; inadequate representation of sea ice dynamic processes; and difficulties in predicting SIC during the melting season. To address these challenges, this study introduces super-resolution SIC Transformer (SR-SICFormer), a deep learning-based, data-driven model designed for fine-scale, long-term Arctic SIC prediction. We also propose a novel physical constraints loss function, ConIce loss, which integrates thermodynamic and dynamic sea ice processes into the training procedure, aiming to improve the model’s predictive accuracy. Satellite remote sensing data is used for training and validating the model. Experimental results demonstrate that SR-SICFormer outperforms traditional statistical and numerical models in extended-range SIC prediction tasks, achieving a 5× spatial super-resolution factor and a 15-day forecast period. The model achieves a root mean square error (RMSE) of 0.0504, a correlation coefficient (r) of 0.9420, a peak signal-to-noise ratio (PSNR) of 30.32 dB, and a structural similarity index measure (SSIM) of 0.9297. For the 2× super-resolution and 60-day forecast during the melting season, SR-SICFormer maintains strong performance, keeping SIC residuals within [-0.3, 0.3]. Additionally, the ConIce loss function effectively preserves both sea ice extent (SIE) and internal concentration distributions, ensuring that extended-range forecast results closely match ground truth in both SIE and concentration.