Iterative-based visualization-oriented fusion scheme for hyperspectral images

This article investigates a novel visualization-based fusion of hyperspectral image bands using an iterative approach. Given a multi-objective function and the pixel-based hyperspectral image fusion method, the optimization process is described as finding the optimal fusion parameters to improve the fusion performance. Accordingly, an iterative-based approach is adopted. In the first step, the fusion process is developed using the pixel-based fusion technique. In the second step, the fused image is produced, and the fusion quality is assessed for multi-objective function construction. For multi-objective formulation, we focus three desired properties of the fused image such as entropy, variance, and smoothness. In the last step, fusion parameters are updated iteratively by examining the objective function. Here, the self-adaptive learning particle swarm optimizer is used to refine the fusion parameters iteratively. Different hyperspectral images, such as Cuprite mining, AVIRIS Indian pines scene, are employed in the evaluation. Quantitative analysis of fused images is carried out through some efficient fusion metrics such as correlation coefficient, entropy, Q-average, ERGAS, SAM, and SID. Experimental results show that the proposed approach outperforms existing methods in terms of both objective function criteria and visual effect.