23 September 2003 Dimensionality reduction in hyperspectral imagery
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Abstract
In this paper we examine how the projection of hyperspectral data into smaller dimensional subspaces can effect the propagation of error. In particular, we show that the nonorthogonality of endmembers in the linear mixing model can cause small changes in band space (as, for example, from the addition of noise) to lead to relatively large changes in the estimated abundance coefficients. We also show that increasing the number of endmembers can actually lead to an increase in the amount of possible error.
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David Gillis, Jeffrey H. Bowles, Michael E. Winter, "Dimensionality reduction in hyperspectral imagery", Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); doi: 10.1117/12.487180; https://doi.org/10.1117/12.487180
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