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23 September 2003 Examining hyperspectral unmixing error reduction due to stepwise unmixing
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Unmixing hyperspectral images inherently transfers error from the original hyperspectral image to the unmixed fraction plane image. In essence by reducing the entire information content of an image down to a handful of representative spectra a significant amount of information is lost. In an image with low spectral diversity that obeys the linear mixture model (such as a simple geologic scene), this loss is negligible. However there exist inherent problems in unmixing a hyperspectral image where the actual number of spectrally distinct items in the image exceeds the resolving ability of an unmixing algorithm given sensor noise. This process is demonstrated here with a simple statistical analysis. Stepwise unmixing, where a subset of end-members is used to unmix each pixel provides a means of mitigating this error. The simplest case of stepwise unmixing, constrained unmixing, is statistically examined here. This approach provides a significant reduction in unmixed image error with a corresponding increase in goodness of fit. Some suggestions for future algorithms are presented.
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Michael E. Winter, Paul G. Lucey, and Donovan Steutel "Examining hyperspectral unmixing error reduction due to stepwise unmixing", Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003);

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