Optimal Component Substitution and Multi-Resolution Analysis Pansharpening Methods Using a Convolutional Neural Network

The fusion of a low spatial resolution multispectral image and a high spatial resolution panchromatic image, i.e., pan-sharpening is an important technique in remote sensing where high resolution imagery is needed. Two of the largest families of such methods are the component substitution (CS) and multi-resolution analysis (MRA) methods. These families of methods can be described by general detail injection schemes which are closely related. In this paper, we propose pansharpening methods which are based on directly implementing these schemes using a convolutional neural network (CNN) such that the mean squared error between the down-sampled fused image and the observed multispectral image is minimized. Using a simulated Pleiades dataset we demonstrate that the proposed approach gives excellent results when compared to other state-of-the-art CS, MRA and CNN methods.