Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced
  Spectral and Spatial Fidelity
Matteo Ciotola Giovanni Poggi Giuseppe Scarpa
In latest years, deep learning has gained a leading role in the pansharpening
of multiresolution images. Given the lack of ground truth data, most deep
learning-based methods carry out supervised training in a reduced-resolution
domain. However, models trained on downsized images tend to perform poorly on
high-resolution target images. For this reason, several research groups are now
turning to unsupervised training in the full-resolution domain, through the
definition of appropriate loss functions and training paradigms. In this
context, we have recently proposed a full-resolution training framework which
can be applied to many existing architectures.
  Here, we propose a new deep learning-based pansharpening model that fully
exploits the potential of this approach and provides cutting-edge performance.
Besides architectural improvements with respect to previous work, such as the
use of residual attention modules, the proposed model features a novel loss
function that jointly promotes the spectral and spatial quality of the
pansharpened data. In addition, thanks to a new fine-tuning strategy, it
improves inference-time adaptation to target images. Experiments on a large
variety of test images, performed in challenging scenarios, demonstrate that
the proposed method compares favorably with the state of the art both in terms
of numerical results and visual output. Code is available online at
https://github.com/matciotola/Lambda-PNN.