An Optimization-Based Approach to Fusion of Hyperspectral Images

Abstract

In this paper we propose a new approach for visualization-oriented fusion of hyperspectral image bands. The proposed technique has been devised to generate the fused image with a certain set of desired properties for a better visualization. The fusion technique should provide a resultant image with a high local contrast without driving individual pixels into over- or under-saturation. We focus on these desired properties of the resultant image, and formulate a multi-objective cost function for the same. We have shown how we can incorporate the constraint of spatial smoothness of the weight vectors, as opposed to the smoothness of the fused image. The solution of this optimization problem has been provided using the Euler-Lagrange technique. By using an appropriate auxiliary variable, we show how the constrained optimization problem can be converted into a computationally efficient unconstrained one. The effectiveness of the proposed technique is substantiated from the visual and quantitative results provided.