Hierarchical Beta Process With Gaussian Process Prior For Hyperspectral Image Super Resolution

NAVEED AKHTAR, Faisal Shafait, Ajmal Saeed Mian

Abstract: Hyperspectral cameras acquire precise spectral information, however, their resolution is very low due to hardware constraints. We propose an image fusion based hyperspectral super resolution approach that employes a Bayesian representation model. The proposed model accounts for spectral smoothness and spatial consistency of the representation by using Gaussian Processes and a spatial kernel in a hierarchical formulation of the Beta Process. The model is employed by our approach to first infer Gaussian Processes for the spectra present in the hyperspectral image. Then, it is used to estimate the activity level of the inferred processes in a sparse representation of a high resolution image of the same scene. Finally, we use the model to compute multiple sparse codes of the high resolution image, that are merged with the samples of the Gaussian Processes for an accurate estimate of the high resolution hyperspectral image. We perform experiments with remotely sensed and ground-based hyperspectral images to establish the effectiveness of our approach.