In recent years, remote sensing images have been widely employed in a series of work such as environmental monitoring. However, the images observed by satellite sensors often have low resolution, which is difficult to meet in-depth research needs. Super resolution (SR) aims to improve image resolution and provides finer spatial details, perfectly compensating for the weaknesses of satellite imagery. Therefore, a back-projection attention network (BPAN) is proposed for SR reconstruction of remote sensing images. The BPAN is composed of the back-projection network and the initial residual attention block. In the back projection network, the iterative error feedback mechanism is adopted to calculate the upper and lower projection errors to guide image reconstruction. In the initial residual attention block, the initial module is introduced to integrate local multilevel features to provide more information for reconstructing detailed textures to focus on the importance of the module to learn different spatial regions adaptively and promote high-frequency information recovery. To evaluate the effectiveness of this method, this study conducts a large number of experiments on AID datasets. The results show that the proposed network model improves the reconstruction performance of traditional deep networks and has significant improvements in visual effects and objective indicators.