Generalized Laplacian Pyramid Pan-Sharpening Gain Injection Prediction Based on CNN

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.