Deep Learning for Enhanced-Resolution Reconstruction of Sentinel-1 Backscatter NRCS in China’s Offshore Seas

High-precision and high-resolution scattering data play a crucial role in remote sensing applications, including ocean environment monitoring, target recognition, and classification. This paper proposes a deep learning-based model aimed at enhancing and reconstructing the spatial resolution of Sentinel-1 backscatter NRCS (Normalized Radar Cross Section) data for China’s offshore seas, including the Bohai Sea, Yellow Sea, East China Sea, Taiwan Strait, and South China Sea. The proposed model innovatively integrates a Self-Attention Feature Fusion based on the Weighted Channel Concatenation (SAFF-WCC) module, combined with the Global Attention Mechanism (GAM) and High-Order Attention (HOA) modules. The feature fusion module effectively regulates the proportion of each feature during the fusion process through weight allocation, significantly enhancing the effectiveness of multi-feature integration. The experimental results show that the model can effectively enhance the fine structural features of marine targets when the resolution is doubled, though the enhancement effect is slightly diminished when the resolution is quadrupled. For high-resolution data reconstruction, the proposed model demonstrates significant advantages over traditional methods under a scale factor of 2 across four key evaluation metrics, including PSNR, SSIM, MS-SSIM, and MAPE. These results indicate that the proposed deep learning-based model is not only well-suited for scattering data from China’s offshore seas but also provides robust support for subsequent research on ocean target recognition, as well as the compression and transmission of SAR data.