Endmember extraction from hyperspectral imagery using a parallel ensemble approach with consensus analysis

Author

Ayuso, F. ; Setoain, J. ; Prieto, M. ; Tenllado, C. ; Tirado, F. ; Plaza, J. ; Plaza, A.

Author_Institution

Dept. Comput. Archit. Complutense, Univ. of Madrid, Madrid, Spain

Volume

5

fYear

2009

fDate

12-17 July 2009

Abstract

We have explored in this paper a framework to test in a quantitative manner the stability of different endmember extraction and spectral unmixing algorithms based on the concept of Consensus Clustering. The idea is to investigate if the sensibility of those algorithms to the number of endmembers can be used to estimate this parameter itself. Preliminary results on synthetic data reveal that the proposed scheme, which can be implemented efficiently in parallel, can compete with state-of-the-art schemes.

Keywords

pattern clustering; statistical analysis; consensus analysis; consensus clustering; endmember extraction; hyperspectral imagery; parallel ensemble approach; spectral unmixing algorithms; Algorithm design and analysis; Clustering algorithms; Computer architecture; Data mining; Hyperspectral imaging; Hyperspectral sensors; Image analysis; Parameter estimation; Principal component analysis; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009

Conference_Location

Cape Town

Print_ISBN

978-1-4244-3394-0

Electronic_ISBN

978-1-4244-3395-7

Type

conf

DOI

10.1109/IGARSS.2009.5417725

Filename

5417725