Convolutional Attention Module for Prisma Pansharpening in the Guided Full Resolution Framework

The increasing availability of hyperspectral satellite-based systems has emphasized the importance of hyperspectral pansharpening. This technique, which fuses a high-resolution panchromatic image with a lower resolution hyperspectral cube, aims to produce an enhanced hyperspectral cube from a spatial point of view while preserving the spectral quality. In this paper we first recall the Guided Full Resolution Frame-work, our novel deep learning training approach, to optimize PRISMA data pansharpening, leveraging both synthetic and real PRISMA data. Then, we introduce an innovative model design and a convolutional attention module as part of architectural enhancement, further improving the performance with respect to the previous work. The results reveal that the minimal computational overhead from this module improves distortion metrics, highlighting its effectiveness in advancing hyperspectral pansharpening techniques.