A method for the super-resolution reconstruction of remote sensing images based on multi-scale feature fusion and an attention mechanism

To handle the existence of remote sensing image super-resolution reconstruction algorithms that are unable to fully utilize multi-scale information, insufficient feature extraction of remote sensing images and lack of learning ability of high-frequency information, This paper introduces a technique for enhancing the resolution of remote sensing images by employing multi-scale feature fusion and attention mechanism. The method firstly uses a single convolutional layer for preliminary feature extraction; secondly, a multiscale feature fusion-attention mechanism residual module (MFAM) is proposed in the nonlinear mapping stage, which uses three different sizes of convolutional kernels for feature extraction and fusion to make fuller use of the detailed parts of remote sensing images, and makes use of the serial-structured channels and the spatial attention mechanism to adaptively extract and enhance the high-frequency information; finally, sub-pixel convolution is used to realize up-sampling and complete the reconstruction for remote sensing images. we conduct comparison experiments of multiple methods on NWPU-RESISC45 and UC Merced datasets in this paper. The experiments in this article demonstrate that the proposed method has shown improvements across various evaluation metrics, resulting in superior super-resolution reconstruction results.