CNN-based pansharpening of multi-resolution remote-sensing images

We propose a convolutional neural network for the pansharpening of remote-sensing imagery. A very compact architecture is designed, which enables accurate training even with small-size datasets. Prior knowledge on the remote sensing domain is taken into account by augmenting the input with several maps of radiometric indices. Extensive experiments on images from various multiresolution sensors show the proposed CNN to outperform the current state of the art in terms of both full-reference and no-reference measures.