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Proceedings Paper

Hyperspectral image visualization based on a human visual model
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Paper Abstract

Hyperspectral image data can provide very fine spectral resolution with more than 200 bands, yet presents challenges for visualization techniques for displaying such rich information on a tristimulus monitor. This study developed a visualization technique by taking advantage of both the consistent natural appearance of a true color image and the feature separation of a PCA image based on a biologically inspired visual attention model. The key part is to extract the informative regions in the scene. The model takes into account human contrast sensitivity functions and generates a topographic saliency map for both images. This is accomplished using a set of linear "center-surround" operations simulating visual receptive fields as the difference between fine and coarse scales. A difference map between the saliency map of the true color image and that of the PCA image is derived and used as a mask on the true color image to select a small number of interesting locations where the PCA image has more salient features than available in the visible bands. The resulting representations preserve hue for vegetation, water, road etc., while the selected attentional locations may be analyzed by more advanced algorithms.

Paper Details

Date Published: 14 February 2008
PDF: 9 pages
Proc. SPIE 6806, Human Vision and Electronic Imaging XIII, 68060N (14 February 2008); doi: 10.1117/12.766703
Show Author Affiliations
Hongqin Zhang, Rochester Institute of Technology (United States)
Honghong Peng, Rochester Institute of Technology (United States)
Mark D. Fairchild, Rochester Institute of Technology (United States)
Ethan D. Montag, Leuze Electronic, Inc. (United States)

Published in SPIE Proceedings Vol. 6806:
Human Vision and Electronic Imaging XIII
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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