Pansharpening is about fusing a high spatial resolution panchromatic image with a simultaneously acquired multispectral image with lower spatial resolution. In this paper, we propose a Laplacian pyramid pansharpening network architecture for accurately fusing a high spatial resolution panchromatic image and a low spatial resolution multispectral image, aiming at getting a higher spatial resolution multispectral image. The proposed architecture considers three aspects. First, we use the Laplacian pyramid method whose blur kernels are designed according to the sensors’ modulation transfer functions to separate the images into multiple scales for fully exploiting the crucial spatial information at different spatial scales. Second, we develop a fusion convolutional neural network (FCNN) for each scale, combining them to form the final multi-scale network architecture. Specifically, we use recursive layers for the FCNN to share parameters across and within pyramid levels, thus significantly reducing the network parameters. Third, a total loss consisting of multiple across-scale loss functions is employed for training, yielding higher accuracy. Extensive experimental results based on quantitative and qualitative assessments exploiting benchmarking datasets demonstrate that the proposed architecture outperforms state-of-the-art pansharpening methods. Code is available at https://github.com/ChengJin-git/LPPN.

全色锐化是将高空间分辨率的全色图像与同时获得的具有较低空间分辨率的多光谱图像融合。在本文中，我们提出了一种拉普拉斯金字塔全色锐化网络架构，用于准确融合高空间分辨率全色图像和低空间分辨率多光谱图像，旨在获得更高空间分辨率的多光谱图像。所提出的架构考虑了三个方面。首先，我们使用拉普拉斯金字塔方法，其模糊核是根据传感器的调制传递函数设计的，将图像分成多个尺度，以充分利用不同空间尺度的关键空间信息。其次，我们为每个尺度开发了一个融合卷积神经网络（FCNN），将它们组合起来形成最终的多尺度网络架构。具体来说，我们为 FCNN 使用递归层来共享金字塔级别之间和内部的参数，从而显着减少网络参数。第三，由多个跨尺度损失函数组成的总损失用于训练，产生更高的准确性。基于利用基准数据集的定量和定性评估的大量实验结果表明，所提出的架构优于最先进的全色锐化方法。代码可在 https://github.com/ChengJin-git/LPPN 获得。由多个跨尺度损失函数组成的总损失用于训练，产生更高的准确性。基于利用基准数据集的定量和定性评估的大量实验结果表明，所提出的架构优于最先进的全色锐化方法。代码可在 https://github.com/ChengJin-git/LPPN 获得。由多个跨尺度损失函数组成的总损失用于训练，产生更高的准确性。基于利用基准数据集的定量和定性评估的大量实验结果表明，所提出的架构优于最先进的全色锐化方法。代码可在 https://github.com/ChengJin-git/LPPN 获得。