Research on Super-Resolution Reconstruction Algorithms for Remote Sensing Images of Coastal Zone Based on Deep Learning

High-resolution multispectral remote sensing imagery is widely used in critical fields such as coastal zone management and marine engineering. However, obtaining such images at a low cost remains a significant challenge. To address this issue, we propose the MRSRGAN method (multi-scale residual super-resolution generative adversarial network). The method leverages Sentinel-2 and GF-2 imagery, selecting nine typical land cover types in coastal zones, and constructs a small sample dataset containing 5210 images. MRSRGAN extracts the differential features between high-resolution (HR) and low-resolution (LR) images to generate super-resolution images. In our MRSRGAN approach, we design three key modules: the fusion attention-enhanced residual module (FAERM), multi-scale attention fusion (MSAF), and multi-scale feature extraction (MSFE). These modules mitigate gradient vanishing and extract image features at different scales to enhance super-resolution reconstruction. We conducted experiments to verify their effectiveness. The results demonstrate that our approach reduces the Learned Perceptual Image Patch Similarity (LPIPS) by 14.34% and improves the Structural Similarity Index (SSIM) by 11.85%. It effectively improves the issue where the large-scale span of ground objects in remote sensing images makes single-scale convolution insufficient for capturing multi-scale detailed features, thereby improving the restoration effect of image details and significantly enhancing the sharpness of ground object edges.