Initialization and convergence of the stochastic mixing model

Michael T. Eismann; Russell C. Hardie

doi:10.1117/12.499680

7 January 2004 Initialization and convergence of the stochastic mixing model

Proceedings Volume 5159, Imaging Spectrometry IX; (2004) https://doi.org/10.1117/12.499680
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

Abstract

An investigation of methods for class mean and covariance initialization of a stochastic mixing model for hyperspectral imagery is described along with other relevant issues concerning algorithm convergence such as updating of the class priors, constraining the mixture classes and the number of fraction levels and endmember classes. The various refinements of the iterative algorithm are presented and tested on synthetically-generated test data as well as real reflective hyperspectral imagery, and recommendations are made concerning how the stochastic mixing model can be best implemented. The results show that the refined stochastic mixng model is a robust approach for unmixing hyperspectral imagery with different levels of complexity.

Citation Download Citation

Michael T. Eismann and Russell C. Hardie "Initialization and convergence of the stochastic mixing model", Proc. SPIE 5159, Imaging Spectrometry IX, (7 January 2004); https://doi.org/10.1117/12.499680

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available