Blind Source Separation for Remote Sensing Images based on the Improved ICA Algorithm

DOI

10.2991/ameii-15.2015.148How to use a DOI?

Keywords

Independent component analysis (ICA); Remote sensing; Bayesian network; Variational approximate algorithm

Abstract

In consideration of some problems including the independence and invariance of components, no noise assumption and the uncertainty of the final solution as well as the inconsistency of the features of remote sensing data in traditional independent component analysis (ICA) model, we put forward a blind source separation algorithm for remote sensing images using variational Bayesian ICA. In the proposed method, the Bayesian network is introduced into the ICA model, the Bayesian inference is used to complete the study of unknown hidden variables, and the computation is optimized by combination with the variational approximation method. Finally, the proposed method is validated by simulation and real remote sensing image tests. The result shows that the variational Bayesian ICA algorithm has both good stability and separation effect, and it overcomes the deficiency of the traditional ICA method in remote sensing application.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Di Shen
AU  - Chengfan Li
AU  - Jingyuan Yin
AU  - Junjuan Zhao
AU  - Dan Xue
PY  - 2015/04
DA  - 2015/04
TI  - Blind Source Separation for Remote Sensing Images based on the Improved ICA Algorithm
BT  - Proceedings of the International Conference on Advances in Mechanical Engineering and Industrial Informatics
PB  - Atlantis Press
SP  - 788
EP  - 793
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-15.2015.148
DO  - 10.2991/ameii-15.2015.148
ID  - Shen2015/04
ER  -