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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 43, Iss. 36 — Dec. 20, 2004
  • pp: 6596–6608

Stochastic Spectral Unmixing with Enhanced Endmember Class Separation

Michael T. Eismann and Russell C. Hardie  »View Author Affiliations


Applied Optics, Vol. 43, Issue 36, pp. 6596-6608 (2004)
http://dx.doi.org/10.1364/AO.43.006596


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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.

© 2004 Optical Society of America

OCIS Codes
(100.2000) Image processing : Digital image processing
(100.5010) Image processing : Pattern recognition

Citation
Michael T. Eismann and Russell C. Hardie, "Stochastic Spectral Unmixing with Enhanced Endmember Class Separation," Appl. Opt. 43, 6596-6608 (2004)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-43-36-6596


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