Since remote sensing images cannot have both high temporal resolution and high spatial resolution, spatiotemporal fusion of remote sensing images has attracted increasing attention in recent years. Additionally, with the successful application of deep learning in various fields, spatiotemporal fusion algorithms based on deep learning have also gradually diversified. We propose a network framework that is based on deep convolutional neural networks that incorporate dilated convolution and multiscale mechanisms, we refer to this network framework as DMNet. In this method, we concatenate the feature maps that need to be fused to avoid using complex fusion methods to introduce noise. Then, the multiscale mechanism can extract the context information of the image at various scales, and make the image details more abundant. By adding skip connections, feature maps in shallow convolutional layers can be obtained to avoid losing important features of the image during the convolution. Additionally, dilated convolution expands the receptive field of the convolution kernel, which is conducive to the extraction of small detail features. To evaluate the robustness of our method, we conduct experiments on two datasets and compare the results with those obtained by six representative spatiotemporal fusion methods. Both intuitive and objective results demonstrate the superior performance of our method.

中文翻译：

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DMNet：使用扩张卷积和多尺度机制进行遥感图像时空融合的网络架构

由于遥感图像不能同时具有高时间分辨率和高空间分辨率，遥感图像的时空融合近年来越来越受到关注。此外，随着深度学习在各个领域的成功应用，基于深度学习的时空融合算法也逐渐多样化。我们提出了一个基于深度卷积神经网络的网络框架，它结合了扩张卷积和多尺度机制，我们将此网络框架称为 DMNet。在这种方法中，我们将需要融合的特征图连接起来，以避免使用复杂的融合方法引入噪声。然后，多尺度机制可以在各种尺度上提取图像的上下文信息，使图像细节更加丰富。通过添加跳过连接，可以获得浅卷积层中的特征图，以避免在卷积过程中丢失图像的重要特征。另外，扩张卷积扩大了卷积核的感受野，有利于小细节特征的提取。为了评估我们方法的稳健性，我们对两个数据集进行了实验，并将结果与​​六种代表性时空融合方法获得的结果进行了比较。直观和客观的结果都证明了我们方法的优越性能。为了评估我们方法的稳健性，我们对两个数据集进行了实验，并将结果与​​六种代表性时空融合方法获得的结果进行了比较。直观和客观的结果都证明了我们方法的优越性能。为了评估我们方法的稳健性，我们对两个数据集进行了实验，并将结果与​​六种代表性时空融合方法获得的结果进行了比较。直观和客观的结果都证明了我们方法的优越性能。