近年来, 在诸如环境监测等一系列工作中, 遥感影像得到了广泛应用. 然而, 目前卫星传感器观测到的影像往往分辨率较低, 很难满足深入研究的需要. 超分辨率(SR)目的是提高图像分辨率, 同时提供更精细的空间细节, 完美地弥补了卫星图像的弱点. 因此, 本文提出了一种反投影注意力网络(back-projection attention network, BPAN)用于遥感图像的超分辨率重建, 该网络由反投影网络和初始残差注意块两部分组成. 在反投影网络中, 通过迭代误差反馈机制计算上下投影误差指导图像重建; 在初始残差注意块中, 引入初始模块融合局部多级特征为重建详细的纹理提供更丰富的信息, 以注意模块自适应地学习不同空间区域的重要性, 促进高频信息的恢复. 为了评价该方法的有效性, 在AID数据集上进行了大量的实验, 结果表明, 本文的网络模型提升了传统深度网络的重建性能, 且在视觉效果和客观指标方面有明显提升.
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.