The existing methods of remote sensing image super-resolution reconstruction based on deep learning have some problems, such as insufficient feature extraction abilities, blurred image edges, and difficult model training. To solve these problems, a super-resolution reconstruction method combining residual channel attention (CA) is proposed. Based on the framework of generative adversarial networks, the residual structure is designed to enhance the ability of deep feature extraction ability for remote sensing images. The CA module is added to extract the deep feature information of remote sensing images, and the shallow features and deep features are fused using the skip connection. The perceptual loss function is combined with the loss function represented by the Wasserstein distance to improve the stability of model training. The experimental results show that this method is superior to the comparison algorithms in the objective evaluation criteria of the peak-signal-to-noise ratio and structural similarity of the reconstructed remote sensing images. After optimizing the model training process, the reconstructed remote sensing images are visually clearer and have sharper edges.

中文翻译：

---

基于剩余通道注意力的单幅遥感影像超分辨率重建

现有的基于深度学习的遥感图像超分辨率重建方法存在特征提取能力不足，图像边缘模糊，模型训练困难等问题。为了解决这些问题，提出了一种结合剩余信道注意力（CA）的超分辨率重建方法。基于生成对抗网络的框架，设计残差结构以增强遥感图像的深度特征提取能力。添加了CA模块以提取遥感图像的深层特征信息，并使用跳过连接融合浅层特征和深层特征。感知损失函数与Wasserstein距离代表的损失函数结合在一起，以提高模型训练的稳定性。实验结果表明，该方法在重建的遥感图像的峰信噪比和结构相似度的客观评价标准上优于比较算法。优化模型训练过程后，重建的遥感图像在视觉上更清晰并且边缘更清晰。