Pansharpening by Convolutional Neural Networks in the Full Resolution Framework

Abstract

In recent years, there has been a growing interest in deep learning-based pansharpening. Thus far, research has mainly focused on architectures. Nonetheless, model training is an equally important issue. A first problem is the absence of ground truths, unavoidable in pansharpening. This is often addressed by training networks in a reduced-resolution domain and using the original data as ground truth, relying on an implicit scale invariance assumption. However, on full-resolution images, results are often disappointing, suggesting such invariance not to hold. A further problem is the scarcity of training data, which causes a limited generalization ability and a poor performance on off-training-test images. In this article, we propose a full-resolution training framework for deep learning-ba