Wavelet-based Bayesian fusion of multispectral and hyperspectral images using Gaussian scale mixture model

Abstract

In this article, a wavelet-based Bayesian fusion framework is presented, in which a low spatial resolution hyperspectral (HS) image is fused with a high spatial resolution multispectral (MS) image by accounting for the joint statistics. Particularly, a zero-mean heavy-tailed model, Gaussian scale mixture model, is employed as the prior, which is believed to be capable of modelling the distribution of wavelet coefficients more accurately than traditional Gaussian model. To keep the calculations feasible, a practical implementation scheme is presented. The proposed approach is validated by simulation experiments for both general HS and MS image fusion as well as the specific case of pansharpening. The experimental results of the proposed approach are also compared with its counterpart, employing a Gaussian prior for performance evaluation.

Keywords:

• Bayesian
• fusion
• Gaussian scale mixture
• hyperspectral
• multispectral

Acknowledgements

This study is supported by (NPU-FFR-JC20100233) NPU Foundation for Fundamental Research Natural Science Basic Research Plan in Shaanxi Province of china (2011JQ8023), and ‘E-star’ Foundation of School of Electronics and Information, NPU.