A TransUNet-Based Intelligent Method for Identifying Internal Solitary Waves in the South China Sea

Internal Solitary Waves (ISWs) play a crucial role in energy transfer among multi-scale oceanic motions. They also have a significant impact on marine transportation and underwater communication. To date, the identification of ISWs has been primarily developed based on Synthetic Aperture Radar (SAR) imagery. However, under severe sea conditions, the characteristics of ISWs at the ocean surface are generally disrupted, complicating their detection through satellite imagery. To mitigate the disturbances caused by severe weather, it is essential to account for ocean thermocline variability. In this study, we propose an automatic identification method for ISWs, utilizing the LLC4320 dataset from the South China Sea region for model training. The main innovations include: (1) The use of model data that incorporates both sea surface and underwater features, enabling accurate identification under rough sea conditions; (2) By incorporating the underwater features of ISWs, a TransUNet-based automatic identification method with some modifications, such as Dynamic Snake Convolution, is developed. The experimental results demonstrate that the model accurately identifies ISWs, achieving a Dice coefficient of 66.32%, Hausdorff_95 (HD95) of 5.27, Mean Pixel Accuracy (MPA) of 85.42%, and Mean Intersection over Union (MIoU) of 73.74% on our dataset, outperforming the other methods.