Stochastic Spectral Unmixing with Enhanced Endmember Class Separation
Document Type
Article
Publication Date
12-2004
Publication Source
Applied Optics
Abstract
Improvements to an algorithm for performing spectral unmixing of hyperspectral imagery based on the stochastic mixing model (SMM) are presented. The SMM provides a method for characterizing both subpixel mixing of the pure image constituents, or endmembers, and statistical variation in the endmember spectra that is due, for example, to sensor noise and natural variability of the pure constituents. Modifications of the iterative, expectation maximization approach to deriving the SMM parameter estimates are proposed, and their effects on unmixing performance are characterized. These modifications specifically concern algorithm initialization, random class assignment, and mixture constraints. The results show that the enhanced stochastic mixing model provides a better statistical representation of hyperspectral imagery from the perspective of achieving greater endmember class separation.
Inclusive pages
6596-6608
ISBN/ISSN
1559-128X
Publisher
OSA: The Optical Society
Volume
43
Peer Reviewed
yes
Issue
36
eCommons Citation
Eismann, Michael T. and Hardie, Russell C., "Stochastic Spectral Unmixing with Enhanced Endmember Class Separation" (2004). Electrical and Computer Engineering Faculty Publications. 70.
https://ecommons.udayton.edu/ece_fac_pub/70
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