  This report proposes a robust method for classifying oceanic and atmospheric
phenomena using synthetic aperture radar (SAR) imagery. Our proposed method
leverages the powerful pre-trained model Swin Transformer v2 Large as the
backbone and employs carefully designed data augmentation and exponential
moving average during training to enhance the model's generalization capability
and stability. In the testing stage, a method called ReAct is utilized to
rectify activation values and utilize Energy Score for more accurate
measurement of model uncertainty, significantly improving out-of-distribution
detection performance. Furthermore, test time augmentation is employed to
enhance classification accuracy and prediction stability. Comprehensive
experimental results demonstrate that each additional technique significantly
improves classification accuracy, confirming their effectiveness in classifying
maritime and atmospheric phenomena in SAR imagery.
