A Super-Resolution Method for Remote Sensing Images of Coastal Aquaculture Ponds Based on Dynamic Sparse Self-Attention

To address the need for cost-effective imagery in precise remote sensing monitoring of coastal aquaculture ponds, a super-resolution method based on a dynamic sparse self-attention mechanism is proposed. The method enhances feature extraction by selectively focusing on the most relevant areas of the image, while maintaining low computational cost. Built on the Transformer architecture, it incorporates interval-based Top-K feature selection and a scale-aware interaction module to better capture multi-scale pond features. Experimental results demonstrate that the proposed approach outperforms existing methods, achieving superior performance in both Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Specifically, PSNR values of 27.25 dB and 29.42 dB are achieved for simulated datasets from Qingdao and Yantai, respectively, while real Sentinel-2 imagery benefits from enhanced texture preservation and noise reduction. This approach provides a promising solution for cost-effective, high-precision monitoring of coastal aquaculture ponds, contributing to the sustainable development of aquaculture resources. The codes are available athttps://github.com/Lrange-212/CAPSR.git.