In the recent years, convolutional neural networks (CNN)-based super resolution (SR) methods are widely used in the field of remote sensing. However, complicated remote sensing images contain abundant high-frequency details, which are difficult to capture and reconstruct effectively. To address this problem, we propose a dense channel attention network (DCAN) to reconstruct high-resolution (HR) remote sensing images. The proposed method learns multi-level feature information and pays more attention to the important and useful regions in order to better reconstruct the final image. Specifically, we construct a dense channel attention mechanism (DCAM), which densely uses the feature maps from the channel attention block via skip connection. This mechanism makes better use of multi-level feature maps which contain abundant high-frequency information. Further, we add a spatial attention block, which makes the network have more flexible discriminative ability. Experimental results demonstrate that the proposed DCAN method outperforms several state-of-the-art methods in both quantitative evaluation and visual quality.

中文翻译：

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基于密集通道注意力网络的遥感图像超分辨率

近年来，基于卷积神经网络（CNN）的超分辨率（SR）方法被广泛应用于遥感领域。然而，复杂的遥感图像包含丰富的高频细节，难以有效捕捉和重建。为了解决这个问题，我们提出了一个密集通道注意网络（DCAN）来重建高分辨率（HR）遥感图像。所提出的方法学习多级特征信息，并更加关注重要和有用的区域，以便更好地重建最终图像。具体来说，我们构建了一个密集通道注意力机制（DCAM），它通过跳过连接密集使用来自通道注意力块的特征图。这种机制更好地利用了包含丰富高频信息的多级特征图。此外，我们添加了一个空间注意力块，使网络具有更灵活的判别能力。实验结果表明，所提出的 DCAN 方法在定量评估和视觉质量方面均优于几种最先进的方法。