ICA-based fusion for colour display of hyperspectral images

Hyperspectral images contain data from a large number of contiguous bands and, therefore, cannot be displayed directly using a colour 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 co...

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Bibliographic Details
Published in:International journal of remote sensing 2011-01, Vol.32 (9), p.2427-2450
Main Authors: Zhu, Yingxuan, Varshney, Pramod K, Chen, Hao
Format: Article
Language:English
Subjects:
Algorithms
Animal, plant and microbial ecology
Applied geophysics
Bands
Biological and medical sciences
Color
Colour
correlation
Criteria
data collection
Earth sciences
Earth, ocean, space
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Internal geophysics
Representations
Teledetection and vegetation maps
Visualization
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Summary:Hyperspectral images contain data from a large number of contiguous bands and, therefore, cannot be displayed directly using a colour 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 colour display purposes is proposed. Correlation coefficient and mutual information (ICA-CCMI) are used as criteria for selecting three suitable independent components for colour representation. In addition, statistical evaluation metrics for the colour 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 colour 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.
ISSN:1366-5901
0143-1161
1366-5901
DOI:10.1080/01431161003698344