Title

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

Issue

36

Peer Reviewed

yes