Deep learning based methods are the state-of-the-art in panchromatic (PAN)/multispectral (MS) fusion (which is generally called “pansharpening”). In this paper, to solve the problem of the insufficient spatial enhancement in most of the existing deep learning based pansharpening methods, we propose a novel pansharpening method based on a residual convolutional neural network (RCNN). Differing from the existing deep learning based pansharpening methods that are mainly devoted to designing an effective network, we make novel changes to the input and the output of the network and propose a simple but effective mapping strategy. This strategy involves utilizing the network to map the differential information between the high spatial resolution panchromatic (HR-PAN) image and the low spatial resolution multispectral (LR-MS) image to the differential information between the HR-PAN image and the high spatial resolution multispectral (HR-MS) image, which is called the “differential information mapping strategy”. Moreover, to further boost the spatial information in the fusion results, the proposed method makes full use of the LR-MS image and utilizes the gradient information of the up-sampled LR-MS image (Up-LR-MS) as auxiliary data to assist the network. Furthermore, an attention module and residual blocks are incorporated in the proposed network structure to maximize the ability of the network to extract features. Experiments on four data sets collected by different satellites confirm the superior performance of the proposed method compared to the state-of-the-art pansharpening methods.

基于深度学习的方法是全色（PAN）/多光谱（MS）融合（通常称为“全锐化”）的最新技术。为了解决大多数现有的基于深度学习的泛锐化方法中空间增强不足的问题，我们提出了一种基于残差卷积神经网络（RCNN）的新型泛锐化方法。与现有的基于深度学习的全屏锐化方法（主要致力于设计有效的网络）不同，我们对网络的输入和输出进行了新颖的更改，并提出了一种简单但有效的映射策略。此策略涉及利用网络将高空间分辨率全色（HR-PAN）图像和低空间分辨率多光谱（LR-MS）图像之间的差异信息映射到HR-PAN图像和高空间分辨率之间的差异信息多光谱（HR-MS）图像，称为“差异信息映射策略”。此外，为了进一步增强融合结果中的空间信息，该方法充分利用了LR-MS图像，并利用上采样LR-MS图像（Up-LR-MS）的梯度信息作为辅助数据。协助网络。此外，在所提出的网络结构中并入了注意模块和剩余块，以最大化网络提取特征的能力。