Hyperspectral Pansharpening Enhanced With Multi-Image Super-Resolution for PRISMA Data

Hyperspectral imagery provides rich spectral information, but commonly suffers from low spatial resolution. This problem can be addressed with hyperspectral pansharpening, which benefits from a high-resolution (HR) panchromatic (PAN) image to enhance the resolution of spectral bands. Another possibility of improving spatial resolution, commonly exploited for remotely sensed images, benefits from multitemporal fusion of images acquired at different revisits of a satellite. However, such a process, termed multi-image super-resolution (MISR), has not been applied to hyperspectral imagery due to the excessive size of hyperspectral cubes. In this article, we propose a new processing pipeline that combines these two approaches. A multitemporal series of PAN images is super-resolved to reconstruct a HR PAN image, which is subsequently used during pansharpening. We demonstrate the benefits of the proposed pipeline for PRISMA imagery, whose hyperspectral cubes [30 m ground sampling distance (GSD)] are associated with PAN images (5 m GSD). The latter are upsampled by a factor of 3× relying on MISR, which allows our pansharpening to achieve the upsampling ratio of 18${\times }$ for PRISMA images. We report and discuss the results of our extensive experiments, which indicate that the proposed approach offers substantial quantitative and qualitative improvement, confirmed by a mean opinion score survey.