Deep residual learning for remote sensed imagery pansharpening

We proposed a deep convolutional network for multi-spectral image pan-sharpening to overcome the drawbacks of traditional methods and improve the fusion accuracy. To break the performance limitation of deep networks, residual learning with specific adaption to image fusion tasks is applied to optimize the architecture of proposed network. Results of adequate experiments support that our model can yield high resolution multi-spectral images with state-of-the-art qualities, as the information in both spatial and spectral domains has been accurately preserved.