Marine oil spills pose significant threats to ecosystems and human health, emphasizing the importance of Synthetic Aperture Radar (SAR) images for reliable, all-weather monitoring. However, current methods face two major challenges. The first is data limitations, including insufficient data quantity and noise, such as speckle noise and distortions introduced during preprocessing. The second is look-alike confusions, which pose challenges in distinguishing oil spills from visually similar phenomena. This paper introduces TransOilSeg, a novel method designed to address these challenges and enhance oil spill detection performance. TransOilSeg employs a Transfer Learning Component (TLC) to integrate data from diverse geographical regions and varying quality, learning general features from multi-source datasets. By leveraging a gradient aggregation algorithm, the model combines features from limited and noisy SAR oil spill datasets, transferring data deficiencies. Additionally, the Adaptive Attention Hybrid Encoder (AAHE) analyzes contextual features and adapts to varying datasets, enabling the model to effectively distinguish oil spills from look-alike phenomena. Comprehensive evaluations across multiple datasets demon strate the robust generalization capability of TransOilSeg. On the M4D dataset, which includes 1,002 training samples, the model achieved a mean Intersection over Union (mIoU) of 61.38% for oil spill detection and 62.41% for look-alike detection. Furthermore, TransOilSeg maintained strong performance when transferred between datasets with varying levels of noise and distortions, demonstrating its adaptability to challenging conditions. These results highlight its potential as a reliable tool for marine oil spill detection and monitoring.

Synthetic aperture radar (SAR) plays a crucial role in monitoring the fine structure of tropical cyclones, but its effectiveness is constrained by limitations such as signal degradation and saturation. To address this challenge, we proposed a transfer learning-based generative adversarial network (GAN) framework with a dilated convolution and attention mechanism for reconstructing inner-core high winds from SAR images. We have employed the principles of transfer learning to adapt pretrained models developed by the Hurricane Weather Research and Forecasting (HWRF) model winds to SAR images during tropical cyclone events for reconstruction. The proposed model can effectively capture the relationship between features in the low-precision areas and global features from SAR images, facilitating tropical cyclone wind speed reconstruction. The utilization of Global Precipitation Measurement (GPM) Level 3 rainfall data facilitates the identification of rainfall regions in 89 SAR images obtained from Radarsat-2 and Sentinel-1A/B missions. Comparison with stepped frequency microwave radiometer (SFMR) data reveals that the model exhibits a bias of −0.69 m/s, a root-mean-square error (RMSE) of 4.08 m/s, and an R value of 0.91 under heavy rainfall conditions (>7.62 mm/h). Remarkably, the GAN model exhibits excellent performance compared with measurements from the soil moisture active passive (SMAP) L-band radiometer, achieving an RMSE of 3.78 m/s. Our findings indicate that deep learning (DL) technology holds significant promise for the reconstruction and monitoring of tropical cyclones through the utilization of SAR imagery.

Synthetic Aperture Radar (SAR) imagery plays an important role in observing tropical cyclones (TCs). However, the C-band attenuation caused by rain bands and the problem of signal saturation at high wind speeds make it impossible to retrieve the fine structure of TCs effectively. In this paper, a dual-level contextual attention generative adversarial network (DeCA-GAN) is tailored for reconstructing SAR wind speeds in TCs. The DeCA-GAN follows an encoder–neck–decoder architecture, which works well for high wind speeds and the reconstruction of a large range of low-quality data. A dual-level encoder comprising a convolutional neural network and a self-attention mechanism is designed to extract the local and global features of the TC structure. After feature fusion, the neck explores the contextual features to form a reconstructed outline and up-samples the features in the decoder to obtain the reconstructed results. The proposed deep learning model has been trained and validated using the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric model product and can be directly used to improve the data quality of SAR wind speeds. Wind speeds are reconstructed well in regions of low-quality SAR data. The root mean square error of the model output and ECMWF in these regions is halved in comparison with the existing SAR wind speed product for the test set. The results indicate that deep learning methods are effective for reconstructing SAR wind speeds.

Offshore wind speed is a critical factor that influences various aspects of human life, and accurate forecasting is of utmost importance for the efficient utilization of offshore resources. In this paper, we present a novel deep-learning-based model for multisite offshore wind speed forecasting along the US East Coast. The proposed model is trained using the collected 2018–2020 National Data Buoy Center buoy data and tested using the 2021–2022 data. By inputting historical wind speed data into the model, simultaneous forecasting results can be obtained for multiple buoy sites through the embedding layer, feature extraction layer, and long short-term memory layer. Notably, the embedding layer, which is specifically engineered to capture spatial dependencies between multiple sites, proves to be highly effective in the context of multisite wind speed forecasting, as substantiated by our conducted ablation experiments. The evaluation metrics display satisfactory results: The 12-h average root mean square error at 1-h forecasting intervals is 2.09 m/s, the correlation coefficient is 0.7, and the mean absolute error is 1.24 m/s. Through case studies, the proposed model demonstrates its effectiveness in forecasting wind speeds during hurricanes, underscoring its potential for use in the offshore wind energy assessment and maritime disaster warning domains.