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27 April 2009 Models for hyperspectral image synthesis and implications for algorithm evaluation
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Abstract
Image synthesis tools provide a means for generating hyperspectral image data without the expense of data collection. An important use of these tools is to provide data for the assessment of image exploitation algorithms. However, the detailed spectral/spatial structure of synthetic images is typically not sufficiently realistic to support the prediction of algorithm performance on real data. In this paper, we develop a new method for hyperspectral texture synthesis that accurately simulates the spectral/spatial structure of real hyperspectral image data. We demonstrate the utility of the new technique by presenting real and synthesized images and by analyzing spectral angle deviation from the mean curves that describe spectral properties. We also demonstrate that a signaturebased detection algorithm has similar performance against real and synthesized hyperspectral backgrounds.
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Subhadip Sarkar and Glenn Healey "Models for hyperspectral image synthesis and implications for algorithm evaluation", Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73340R (27 April 2009); https://doi.org/10.1117/12.818344
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