A Detail-Preserving Cross-Scale Learning Strategy for CNN-Based Pansharpening

The fusion of a single panchromatic (PAN) band with a lower resolution multispectral (MS) image to raise the MS resolution to that of the PAN is known as pansharpening. In the last years a paradigm shift from model-based to data-driven approaches, in particular making use of Convolutional Neural Networks (CNN), has been observed. Motivated by this research trend, in this work we introduce a cross-scale learning strategy for CNN pansharpening models. Early CNN approaches resort to a resolution downgrading process to produce suitable training samples. As a consequence, the actual performance at the target resolution of the models trained at a reduced scale is an open issue. To cope with this shortcoming we propose a more complex loss computation that involves simultaneously reduced and full resolution training samples. Our experiments show a clear image enhancement in the full-resolution framework, with a negligible loss in the reduced-resolution space.