Cart (Loading....) | Create Account
Close category search window
 

Spatial sparsity-based blind source separation method including non-negative matrix factorization for multispectral image unmixing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Karoui, M.S. ; Div. Obs. de la Terre, Centre des Tech. Spatiales, Arzew, Algeria ; Deville, Y. ; Hosseini, S. ; Ouamri, A.

In this paper, we propose an unsupervised spatial method in order to unmix each pixel of a remote sensing multispectral image. This method is related to the blind source separation (BSS) problem, and is based on sparse component analysis (SCA) and non-negative matrix factorization (NMF). Our approach consists in identifying the mixing matrix in the first stages, by using a spatial correlation-based SCA method, combined with clustering. An NMF method is used to extract spatial sources in the last stage. The overall proposed method is applicable to the globally underdetermined BSS model in multispectral remote sensing images. An experiment based on realistic synthetic mixtures is performed to evaluate the feasibility of the proposed approach. We also show that our method significantly outperforms the sequential maximum angle convex cone (SMACC) method.

Published in:

Electronics, Control, Measurement and Signals (ECMS), 2011 10th International Workshop on

Date of Conference:

1-3 June 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.