Scene-Adaptive Remote Sensing Image Super-Resolution Using a Multiscale Attention Network

Abstract

Remote sensing image super-resolution has always been a major research focus, and many deep-learning-based algorithms have been proposed in recent years. However, since the structure of remote sensing images tends to be much more complex than that of natural images, several difficulties still remain for remote sensing images super-resolution. First, it is difficult to depict the nonlinear mapping between high-resolution (HR) and low-resolution (LR) images of different scenes with the same model. Second, the wide range of scales within the ground objects in remote sensing images makes it difficult for single-scale convolution to effectively extract features of various scales. To address the above-mentioned issues, we propose a multiscale attention network (MSAN) to extract the multilevel features of remote sensing images. The basic component of MSAN is the multiscale activation feature fusion block (MAFB). In addition, a scene-adaptive super-resolution strategy for remote sensing images is employed to more accurately describe the structural characteristics of different scenes. The experiments undertaken on several data sets confirm that the proposed algorithm outperforms the other state-of-the-art algorithms, in both evaluation indices and visual results.