A Multi-Scale Gated Feedforward Attention Network for Super-Resolution Reconstruction of Remote Sensing Images in Plastic-Mulched Planting Regions

The rapid expansion of Plastic-Mulched Landcover (PML)，characterized by its relatively small size and short lifespan, necessitates precisely mapping PML using High-Resolution Remote Sensing Imagery (HRRSI). However, the high costs and limited temporal resolution of acquiring HRRSI pose significant challenges for precise PML identification. Remote Sensing Image Super-Resolution (RSISR) offers a viable solution by reconstructing high-resolution images from lower-resolution inputs, enhancing PML detection capabilities. This study, based on hybrid attention transformer, develops a Multi-Scale Gated Feedforward Attention Network (MSG-FAN) for super-resolution reconstruction of Sentinel-2 data to meter-level resolution. The main contributions include: (1) Construction of a PML RS dataset comprising 5300 pairs of 10-m Sentinel-2 and corresponding 2.5-m Gaofen-2 and Planetscope images from eight globally selected plastic-mulched planting regions. (2) Development of the MSG-FAN model, which enhances multi-channel, multi-scale and global attentions by integrating Gated Multi-Scale Feedforward Layer (GMS-FL), Top-k Token Selective Attention (TTSA) module and Global Context Attention (GCA) module. (3) Demonstration that MSG-FAN outperforms nine state-of-the-art deep learning-based super-resolution networks, achieving an average PSNR 30.81 and SSIM of 0.7287. Our proposed MSG-FAN model advances RSISR techniques and addresses critical challenges in monitoring plastic-mulched planting regions.