A Bayesian approach to visualization-oriented hyperspectral image fusion

• Fusion posed as an estimation problem where each sensor relates to the fused image through a model of image formation.
• Parameters of the model have been defined to be proportional to the visual or perceived quality of the scene captured.
• Fused image is obtained as a MAP estimate with a TV norm-based prior.
• The use of TV norm preserves the sharp discontinuities- edges and boundaries in the fused image.

In this paper, we propose a Bayesian approach towards fusion of hyperspectral images for the purpose of efficient visualization. Fusion has been posed as an estimation problem where the observed hyperspectral bands have been related to the fused image through a first order model of image formation. The parameters of the model indicate the quality of the pixel captured locally. As visualization is our primary aim of fusion, we expect higher contribution of the “visually important” pixels towards the final fused image. We propose a two-step framework for fusion of hyperspectral image, where the first step identifies the quality of each pixel of the data based on some of the local quality measures of the hyperspectral data. Subsequently, we formulate the problem of the estimation of the fused image in a MAP framework. We incorporate the total variation (TV) norm-based prior which preserves the sharp discontinuities in the fused image. The fused images, thus, appear sharp and natural where the edges and boundaries have been retained. We have provided visual as well as quantitative results to substantiate the effectiveness of the proposed technique.