Hyperspectral pansharpening using convex optimization and collaborative total variation regularization

Hyperspectral pansharpening is a challenging research area and several methods have been recently developed to fuse low resolution hyperspectral and high resolution panchromatic images. In this paper we focus on a recent regularization method, called Collaborative Total Variation, exploiting a convex optimization algorithm. We evaluate the effectiveness of this novel approach in comparison to existing methods, and assess the performances on two datasets: a synthetic scene mimicking the characteristics of the Hyperion and ALI sensors and the Pavia University dataset.