This study investigated appropriate methodologies for displaying hyperspectral imagery based on knowledge of human color vision as applied to Hyperion and AVIRIS data. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were used to reduce the data dimensionality in order to make the data more amenable to visualization in three-dimensional color space. In addition, these two methods were chosen because of their underlying relationships to the opponent color model of human color perception. PCA and ICA-based visualization strategies were then explored by mapping the first three PCs or ICs to several opponent color spaces including CIELAB, HSV, YCrCb, and YUV. The gray world assumption, which states that given an image with sufficient amount of color variations, the average color should be gray, was used to set the mapping origins. The rendered images are well color balanced and can offer a first look capability or initial classification for a wide variety of spectral scenes.

Date of creation, presentation, or exhibit



Proceedings of Algorithms and Technolgies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII 6233 (2006) 62330X-1-12 "Perceptual display strategies of hyperspectral imagery based on PCA and ICA," Proceedings of Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII. International Society of Optical Engineers. Held in Orlando, Florida: 17-21 April 2006.. Copyright 2006 Society of Photo-Optical Instrumentation Engineers. This paper was published in the Proceedings of Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII SPIE Vol. 6233 and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. We would like to thank Kodak for their financial support for this project. ISSN: 0277-786X Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type

Conference Proceeding

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


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