In recent decades, satellites have played a pivotal role in observing ocean dynamics, providing diverse datasets with varying spatial resolutions. Notably, within these datasets, sea surface height (SSH) data typically exhibit low resolution, while sea surface temperature (SST) data have significantly higher resolution. This study introduces a Transfer Learning-enhanced Generative Adversarial Network (TLGAN) for reconstructing high-resolution SSH fields through the fusion of heterogeneous SST data. In contrast to alternative deep learning approaches that involve directly stacking SSH and SST data as input channels in neural networks, our methodology utilizes bifurcated blocks comprising Residual Dense Module and Residual Feature Distillation Module to extract features from SSH and SST data, respectively. A pixelshuffle module-based upscaling block is then concatenated to map these features into a common latent space. Employing a hybrid strategy involving adversarial training and transfer learning, we overcome the limitation that SST and SSH data should share the same time dimension and achieve significant resolution enhancement in SSH reconstruction. Experimental results demonstrate that, when compared to interpolation method, TLGAN effectively reduces reconstruction errors and fusing SST data could significantly enhance in generating more realistic and physically plausible results.

The characterization of targets by electromagnetic (EM) scattering and underwater acoustic scattering is an important object of research in these two related fields. However, there are some difficulties in the simulation and measurement of the scattering by large targets. Consequently, a similarity study between acoustic and EM scattering may help to share results between one domain and the other and even provide a general reference method for the simulation of scattering characteristics in both fields. Based on the method of physical optics, the similarity between the EM scattering of conductors and the acoustic scattering of soft/hard targets and the similarity between the EM scattering of dielectrics and the acoustic scattering of elastics are studied. In particular, we derive how to transfer quantities from one domain into another so that similar scattering patterns arise. Then, according to these transfer rules, the EM scattering and acoustic scattering of three typical targets with different types of boundaries were simulated and measured, and the simulated EM scattering and acoustic scattering curves were found to be in perfect agreement, with correlation coefficients above 0.93. The correlation coefficients between the electromagnetic and acoustic scattering patterns were above 0.98, 0.91, and 0.65 for three typical targets. The simulated and measured scattering results verify the proposed similarity theory of EM and acoustic scattering, including the transfer from one domain into the other and the conditions of EM and acoustic scattering, and illustrate that the acoustic scattering characteristic of the target can be simulated using the EM scattering based on the derived conditions and vice versa.

Accurate prediction of Arctic sea ice concentration (SIC) is critical for understanding climate change and ensuring safe Arctic shipping. However, conventional U-Net architectures often degrade deep features and lack explicit physical constraints. To address this, we propose a physics-guided U-Net with attention residual connections, in which physical forcing steers feature learning at the architecture level rather than entering solely as loss-level constraints. An attention residual mechanism enables adaptive multi-scale feature fusion across layers, while a physics-constrained module is embedded throughout the encoder–decoder to enforce physical consistency. Thermodynamic and dynamic variables (sea surface temperature, 2 m air temperature, and 10 m wind components) are fused via cross-modal attention and physics-guided skip connections. A dual-input framework captures local fine-scale ice edge details and large-scale Arctic background fields simultaneously. The composite loss combines a weighted Charbonnier term and a freezing loss to better penalize ice edge mismatches and enforce freeze/melt physics. Experiments using National Snow and Ice Data Center (NSIDC) and ERA5 data demonstrate that our model outperforms six representative deep learning baselines at the 1-month lead under a unified multi-seed protocol, with all pairwise improvements statistically significant, and retains substantial skill at a 6-month lead, accurately reproducing the September minimum, with ice edges closely tracking observations. These results confirm that the physics-guided attention residual U-Net enhances spatial fidelity and physical consistency of subseasonal-to-seasonal SIC predictions, thereby supporting robust, medium-to-long-term assessment of Arctic navigability windows.

This paper proposes a dynamic obstacle-avoidance algorithm for unmanned surface vehicles (USVs) that combines a Fuzzy-enhanced Dynamic Window Approach (FDWA) with an Improved Bidirectional A*–Improved Grey Wolf Optimizer (IBA*–IGWO) framework. Firstly, the traditional dynamic window method (DWA) is improved by adopting an initial heading angle optimization strategy to reduce the heading deviation of unmanned vessels during cruising. Secondly, a fuzzy controller is introduced, which can adaptively adjust the weight coefficients in the cost function of the DWA algorithm based on the current position of the unmanned vessel, surrounding environmental information, etc., to improve obstacle avoidance ability and adaptability in different environments. Finally, using the global static cruise route provided by the IBA*–IGWO algorithm, key nodes are selected as local endpoints for the FDWA algorithm to ensure that the unmanned vessel can perform cruise tasks according to the optimal plan during navigation and make dynamic adjustments in case of emergencies. The simulation results demonstrate the feasibility of the proposed method in handling unknown and dynamic obstacles under the current grid-based experimental settings, while enabling the USV to return to the pre-planned global route after local obstacle avoidance. These results provide a basis for further development toward more robust and rule-aware autonomous navigation in realistic maritime environments.

Cloud detection in remote sensing imagery is a research hotspot in the field of image processing, and accurately detecting and segmenting cloud regions is crucial for improving the utilization efficiency of remote sensing data. However, standard convolutional neural networks face limitations in modeling the complex spatial structures of clouds. To address these challenges, this paper proposes a cloud detection method based on DDS-DeeplabV3+. First, a lightweight design of the Xception network is adopted to control model complexity, and part of its standard convolutional layers are replaced with Deformable Convolutional Networks (DCN), which enhances the capability of the model to capture geometric features of irregular cloud formations. Second, a Dual-Branch Collaborative Mechanism (DCM) that integrates global context modeling with local detail perception is designed to reconstruct the Atrous Spatial Pyramid Pooling (ASPP) module, thereby improving performance in handling complex scenes and fine boundary delineation. Finally, the SimAM (Simple, Parameter-Free Attention Module) is incorporated into the decoder module, enhancing thin cloud detection capability. Experimental results on the Landsat-8 and GF-1 datasets show that the proposed model achieves Mean Intersection over Union (MIoU) values of 92.61% and 94.04%, respectively, outperforming other comparative methods and demonstrating its superior performance in cloud detection tasks.