FuBay: An Integrated Fusion Framework for Hyperspectral Super-Resolution Based on Bayesian Tensor Ring

Fusion with corresponding finer-resolution images has been a promising way to enhance hyperspectral images (HSIs) spatially. Recently, low-rank tensor-based methods have shown advantages compared with other kind of ones. However, these current methods either relent to blind manual selection of latent tensor rank, whereas the prior knowledge about tensor rank is surprisingly limited, or resort to regularization to make the role of low rankness without exploration on the underlying low-dimensional factors, both of which are leaving the computational burden of parameter tuning. To address that, a novel Bayesian sparse learning-based tensor ring (TR) fusion model is proposed, named as FuBay. Through specifying hierarchical sprasity-inducing prior distribution, the proposed method becomes the first fully Bayesian probabilistic tensor framework for hyperspectral fusion. With the relationship between component sparseness and the corresponding hyperprior parameter being well studied, a component pruning part is established to asymptotically approaching true latent rank. Furthermore, a variational inference (VI)-based algorithm is derived to learn the posterior of TR factors, circumventing nonconvex optimization that bothers the most tensor decomposition-based fusion methods. As a Bayesian learning methods, our model is characterized to be parameter tuning-free. Finally, extensive experiments demonstrate its superior performance when compared with state-of-the-art methods.

I. Introduction

Hyperspectral imaging technology is able to cover a wide range of electromagnetic spectrum thus carries rich and comprehensive information of objectives. Recently, hyperspectral image (HSI) has been taken into lots of applications such as land-cover classification [1], [2], anomaly detection [3], [4], and object tracking [5]. However, present hyperspectral sensors suffer from spatial resolution loss while maintaining spectral details [6], which motivates the employment of multispectral image (MSI) and panchromatic (PAN) image that are of high spatial resolution to perform hyperspectral super-resolution, which, also termed as spatial–spectral fusion, is now a popular scheme to generate high spatial resolution HSI (HR-HSI).