ICA-based fusion for color display of hyperspectral images

Hyperspectral images, contain data from a large number of contiguous bands and, therefore, cannot be displayed directly using a color display system. In this paper, an independent component analysis-based (ICA-based) approach for the problem of fusing hyperspectral images to three-band images for color display purposes is proposed. Correlation coefficient and mutual information (ICA-CCMI) are used as criteria for selecting three suitable independent components for color representation. In addition, statistical evaluation metrics for the color display results of hyperspectral images are provided and discussed in light of different visualization goals. A new quality metric motivated by the quality index is developed to evaluate the structural information of the color display images. The performance of our approach is validated by applying it to three hyperspectral image datasets. The experimental results demonstrate promising performance for the ICA-CCMI algorithm, compared with existing principal component analysis-based (PCA-based) methods for visualization of hyperspectral images.