Pan-sharpening aims to fuse a low-spatial-resolution multispectral (MS) image with an associated higher resolution panchromatic image (PAN) in order to produce a high-resolution MS (HRMS) image to overcome physical limitation of satellite sensors. In this letter, we propose a new generalized Laplacian pyramid gain injection prediction based on convolutional neural networks (GIP-CNN) for pan-sharpening, which estimates the values of the injection gains for each MS band to complement it with spatial details extracted from the PAN image. The experimental results on images from different satellites show that GIP-CNN can achieve higher performances with respect to the state-of-the-art and new CNN-based methods in both spatial and spectral qualities.

中文翻译：

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基于CNN的广义拉普拉斯金字塔泛锐化增益注入预测

全色锐化旨在将低空间分辨率多光谱 (MS) 图像与相关联的更高分辨率全色图像 (PAN) 融合，以生成高分辨率 MS (HRMS) 图像，以克服卫星传感器的物理限制。在这封信中，我们提出了一种新的基于卷积神经网络 (GIP-CNN) 的广义拉普拉斯金字塔增益注入预测，用于全色锐化，它估计每个 MS 波段的注入增益值，以补充从PAN 图像。对来自不同卫星的图像的实验结果表明，相对于最先进的和基于 CNN 的新方法，GIP-CNN 可以在空间和光谱质量方面实现更高的性能。