Hyperspectral pansharpening: a review

TL;DR

This review paper compares various hyperspectral pansharpening techniques, analyzing their effectiveness and robustness across datasets, and provides a MATLAB toolbox for community use.

Contribution

It offers a comprehensive comparison of eleven hyperspectral pansharpening methods, including implementation details and performance evaluation across multiple datasets.

Findings

Different methods show varying effectiveness depending on the dataset.

Hybrid and Bayesian methods generally outperform others in robustness.

The MATLAB toolbox facilitates further research and application.

Abstract

Pansharpening aims at fusing a panchromatic image with a multispectral one, to generate an image with the high spatial resolution of the former and the high spectral resolution of the latter. In the last decade, many algorithms have been presented in the literature for pansharpening using multispectral data. With the increasing availability of hyperspectral systems, these methods are now being adapted to hyperspectral images. In this work, we compare new pansharpening techniques designed for hyperspectral data with some of the state of the art methods for multispectral pansharpening, which have been adapted for hyperspectral data. Eleven methods from different classes (component substitution, multiresolution analysis, hybrid, Bayesian and matrix factorization) are analyzed. These methods are applied to three datasets and their effectiveness and robustness are evaluated with widely used performance indicators. In addition, all the pansharpening techniques considered in this paper have been implemented in a MATLAB toolbox that is made available to the community.