RFCFormer: A Dual-Stream Transformer Architecture Integrating Gramian Angular Field Representations for Retrieving Evaporation Duct Refractivity From Radar Sea Clutter

This study introduces RFCFormer, an innovative framework for retrieving evaporation duct refractivity from radar sea clutter, which synergistically integrates a dual-stream Transformer architecture with Gramian angular field (GAF) methodology. The framework first converts the input sea clutter into two distinct GAF matrices, effectively transforming the raw clutter into more manageable sparse matrix representations. This transformation enhances the model’s ability to identify underlying patterns within the sea clutter, improving the accuracy of the mapping between clutter characteristics and duct parameters. The dual-stream Transformer architecture then utilizes parallel processing pathways, where coordinated self-attention mechanisms process the GAF matrices in depth, facilitating the integration of multiscale clutter features through cross-stream feature fusion. Experimental evaluations show that RFCFormer surpasses existing approaches in both inversion accuracy and generalization capability. Furthermore, the model achieves simultaneous retrieval of four duct parameters while maintaining adaptability to diverse sea states, which conclusively demonstrates its practical utility in real-world marine environments.