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 panchromatic 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, has not been applied to hyperspectral imagery due to the excessive size of hyperspectral cubes. In this paper, we propose a new processing pipeline that combines these two approaches. A multitemporal series of panchromatic images is super-resolved to reconstruct a high-resolution panchromatic 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) are associated with panchromatic images (5 m ground sampling distance). The latter are upsampled by a factor of 3× relying on multi-image super-resolution, which allows our pansharpening to achieve the upsampling ratio of 18<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\times }$</tex-math></inline-formula> 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.