Global sparse attention network for remote sensing image super-resolution

Recently, remote sensing images have become popular in various tasks, including resource exploration. However, limited by hardware conditions and formation processes, the obtained remote sensing images often suffer from low-resolution problems. Unlike the high cost of hardware to acquire high-resolution images, super-resolution software methods are good alternatives for restoring low-resolution images. In addition, remote sensing images have a common nature that similar visual patterns repeatedly appear across distant locations. To fully capture these long-range satellite image contexts, we first introduce the global attention network super-resolution method to reconstruct the images. This network improves the performance but introduces unessential information while significantly increasing the computational effort. To address these problems, we propose an innovative method named the global sparse attention network (GSAN) that integrates both sparsity constraints and global attention. Specifically, our method applies spherical locality sensitive hashing (SLSH) to convert feature elements into hash codes, constructs attention groups based on the hash codes, and computes the attention matrix according to similar elements in the attention group. Our method captures valid and useful global information and reduces the computational effort from quadratic to asymptotically linear regarding the spatial size. Extensive qualitative and quantitative experiments demonstrate that our GSAN has significant competitive advantages in terms of performance and computational cost compared with other state-of-the-art methods.