{Giuseppe Scarpa Matteo Ciotola}

Abstract

In the last few years, there has been a renewed interest in data fusion techniques, and, in particular, in pansharpening due to a paradigm shift from model-based to data-driven approaches, supported by the recent advances in deep learning. Although a plethora of convolutional neural networks (CNN) for pansharpening have been devised, some fundamental issues still wait for answers. Among these, cross-scale and cross-datasets generalization capabilities are probably the most urgent ones since most of the current networks are trained at a different scale (reduced-resolution), and, in general, they are well-fitted on some datasets but fail on others. A recent attempt to address both these issues leverages on a target-adaptive inference scheme operating with a suitable full-resolution loss. On the downside, such an approach pays an additional computational overhead due to the adaptation phase. In this work, we propose a variant of this method with an effective target-adaptation scheme that allows for the reduction in inference time by a factor of ten, on average, without accuracy loss. A wide set of experiments carried out on three different datasets, GeoEye-1, WorldView-2 and WorldView-3, prove the computational gain obtained while keeping top accuracy scores compared to state-of-the-art methods, both model-based and deep-learning ones. The generality of the proposed solution has also been validated, applying the new adaptation framework to different CNN models.