Dimensionality Reduction of Hyperspectral Images for Color Display using Segmented Independent Component Analysis

The problem of dimensionality reduction for color representation of hyperspectral images has received recent attention. In this paper, several independent component analysis (ICA) based approaches are proposed to reduce the dimensionality of hyperspectral images for visualization. We also develop a...

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Bibliographic Details
Main Authors: Yingxuan Zhu, Varshney, P.K., Hao Chen
Format: Conference Proceeding
Language:English
Subjects:
dimensionality reduction
Displays
Higher order statistics
Humans
Hyperspectral imaging
Hyperspectral sensors
ICA
Image color analysis
Image segmentation
Independent component analysis
Mutual information
Visualization
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Summary:The problem of dimensionality reduction for color representation of hyperspectral images has received recent attention. In this paper, several independent component analysis (ICA) based approaches are proposed to reduce the dimensionality of hyperspectral images for visualization. We also develop a simple but effective method, based on correlation coefficient and mutual information (CCMI), to select the suitable independent components for RGB color representation. Experimental results are presented to illustrate the performance of our approaches.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2007.4379963