14 February 2008 Hyperspectral image visualization based on a human visual model
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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.
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Hongqin Zhang, Honghong Peng, Mark D. Fairchild, Ethan D. Montag, "Hyperspectral image visualization based on a human visual model", Proc. SPIE 6806, Human Vision and Electronic Imaging XIII, 68060N (14 February 2008); doi: 10.1117/12.766703; https://doi.org/10.1117/12.766703
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